组合 1

Review

A Review of Performance-Oriented Architectural Design and Optimization in the Context of Sustainability: Dividends and Challenges

Shaoxiong Li 1 , Le Liu 1 and Changhai Peng 1,2,*

School of Architecture, Southeast University, Nanjing 210096, China; 230159310@seu.edu.cn (S.L.); 220180022@seu.edu.cn (L.L.)
Key Laboratory of Urban and Architectural Heritage Conservation (Southeast University), Ministry of

Education, Nanjing 210096, China

* Correspondence: pengchanghai@seu.edu.cn; Tel.: +86-25-8379-2484 or 138-5168-2989; Fax: +86-25-8379-3232

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Received: 30 December 2019; Accepted: 12 February 2020; Published: 14 February 2020

updates

Abstract: As most countries have widespread and growing concerns about the sustainable development of society, the requirement to continuously reduce energy consumption poses challenges for the architecture, engineering and construction (AEC) industry. Performance-oriented architectural design and optimization, as a novel design philosophy and comprehensive evolution technology, has been accepted by architects, engineers, and stakeholders for a period of time. Performance in the context of architecture is a widely discussed definition that has long shown a correlation with visual and cultural attributes. Shifting the paradigm of sustainable development while ensuring that the function and aesthetics of the building are not overlooked has been the focus of public attention. Considering the core design elements that affect energy conservation and style performance, the design and optimization of building envelopes, form, and shading systems were selected as research materials. From the perspective of epistemology and methodology, a systematic review of 99 papers was conducted to promulgate the latest development status of energy-efficiency design. This paper manifests a detailed analysis of the design patterns, research features, optimization objectives, and techniques of current approaches. The review found that performance-oriented design optimization can benefit the entire industry from the heuristic knowledge base and the expansion of the design space while maintaining sustainability. In contrast, challenges such as tools, skills, collaboration frameworks, and calibration models are highlighted.

Keywords: performance-oriented; sustainable architecture; building envelope; building form; shading system; design and optimization; AEC industry

Introduction

1. Background

In the context of a broad scope of architectural dialogues, the concept of performance is a particularly prominent and lasting one [1]. Aesthetic and cultural expression has long been the focus of architectural design. Theorists and practitioners are committed to ensuring that a building reveals the unique attributes of its visual aesthetic from all aspects of form, space, order, color, and detail [2]. But the overall context of building performance is highly dynamic. It is defined as a concept that describes a building’s ability to perform its tasks and functions, the degree of construction control over the delivery process, and its success as a presentation or entertainment. There are various trends in the architecture, engineering and construction (AEC) industry which are affected by performance requirements. In addition, it also maps the complexity of the research area [3]. With the

Sustainability 2020, 12, 1427; doi:10.3390/su12041427 www.mdpi.com/journal/sustainability

energy crisis and the rapid deterioration of global climate and environment, for the long-term and sustainable development of human society has become the primary problem of all countries in the world [4]. With such an urgent agenda, the energy efficiency of buildings and even cities has become an unavoidable topic for architects, planners, engineers, and other stakeholders.

Research and practice in the domain of building energy performance has become the mainstream and launched Web of Science to conduct a keyword-based search of articles and abstracts in related research areas (Figure 1). Building energy performance is considered as the keyword. Records indicate that the earliest work came from 1976. The number has gradually increased since 1990 and has changed drastically in the last decade. Correspondingly, the evaluation system of building sustainability emerges in the world. A comprehensive building performance assessment method was released in the UK in 1990 (BREEAM), and the latest version was released in 2014 [5]. LEED (USA) certification standards were issued in 1998 [6]. Followed by CASBEE (Japan), GreenStar (Australia), HQE (France), and DGNB (Germany) [7]. China’s Ministry of Housing and Urban-Rural Development (MOHURD) issued the first green building standard in 2006 and updated it in 2019 [8]. A large number of well-known tools are used during the design phase to help practitioners calculate, simulate, and evaluate building energy consumption [9]. In a sense, especially in the last two decades, it has witnessed a shift in the architecture, engineering and construction (AEC) industry to a sustainable era.

Figure 1. Keywords for building energy performance related issues.

Combining design with the natural environment, technologies, or methods aimed at optimizing efficiency in the starting chapters of work are constantly mentioned. Professional terms such as “performance-based design” and “performance-driven design” are considered a new design philosophy [10]. Review the development history of regional architectural practice in the “pre-tech” era. Environmental characteristics have proven to be an important influencing factor in architectural expression. There is a significant relationship between the specific characteristics of the building and its climate region. It is not just a coincidence that groups of different faiths and cultures have found solutions between creative building activities and the climate in which they live [11]. At the same time, the voice of doubt followed. Too much attention to whether building energy efficiency will lead to the lack of traditional design aesthetics will inevitably cause anxiety. Interestingly, some scholars believe that most of the components that affect the energy structure of buildings contribute little to the style

and visual characteristics associated with contemporary architecture [12]. Strictly speaking, this is not just a debate on design methodology. A re-examination of all doubts has revealed deeper concerns. Extensive multidisciplinary cooperation and the growth of the technical team have indeed brought about improvements in efficiency and quality. However, the concept of openness and the lack of a clear consensus on the operation mode could lead to deviation and incompatibility.

1. Hypotheses and Purposes of the Work

The main task of this review is to carry out a systematic and quantitative study of energy optimization methods and experiments in the initial stage with a focus on reviewing and analyzing the latest technologies from the perspective of design decision makers. The reasons for the resolution are listed as follows: First and foremost, unlike pure visual design art, architectural design is a comprehensive discipline that combines personal preferences and rational logic. With the energy consumption targets in the list becoming the top priority, the workload of various categories has increased significantly. Compared with the traditional graphic language, how to deal with the data-based design method is a new problem faced by architects. Secondly, the professional skills required of architects are more stringent than in any previous period, and there can be subtle tradeoffs in design aesthetics and instrumental rationality. Last but not least, the complexity of the building system requires different professionals in the technical team to work together in the workflow, which is essentially different from the previous work mode. Architecture and energy provide architects and building theorists with more lasting arguments for environmental design decisions. The hypotheses involved in sustainable design are that the way in which energy is used can be transformed (directly or indirectly) into the visible aspects of architectural styles and their specific forms. In other words, different “performance” synergies are implemented in the optimization process. In summary, the purpose of this review is to collect and analyze relevant literature, and discuss the actual benefits and potential conflicts of performance-oriented building design and optimization in the context of sustainability.

1. Scope and Specifications

Web of Science and ScienceDirect were used to conduct a global search for related journals and conference papers. The keywords included “building”, “performance”, “energy consumption”, “design”, and “optimization”. Various combinations of the mentioned keywords were made. The final inclusion deadline for all works was from 1990 to 2019. Nevertheless, the number of articles accessed was still enormous. The purpose of this paper is to explore the implicit relevance between energy performance-oriented optimization design and architectural form and style innovation. Therefore, in the further stage of literature selection, several restrictions were added:

Whether to minimize energy cost as the driving force at the beginning stage is the first and crucial factor in screening. The thesis must clearly indicate the energy consumption benefits brought by the design or optimization scheme. Under this prerequisite, the research content must contain intuitive visual elements or style features to make it a unique architectural design vocabulary. Suppose a research paper aims to reduce all energy-related expenditures of a building and provide inspirational design knowledge to stakeholders, obviously it is considered the core content.
If the research materials use building shapes or envelopes to expand the potential of solar energy, then, it is qualified. In addition, research topics include thermal comfort, carbon emissions, and life-cycle costs, all of which meet standards because they also contribute to sustainable development.
Energy consumption optimization based on building material details is undoubtedly a complex and decisive research field. Herein, the main interest is how the principles of appearance and form variations affect the energy use of buildings. In addition, reference studies evaluated the impact of geometric changes and material considerations on building energy consumption. The results

show that the sensitivity of material properties and geometric factors depends on the specific design goals. The local sensitivity index of the design variable of the geometric pattern under specific project types and climatic conditions is even higher than the material characteristics [13]. With due consideration of the existing workflow, the lag of the complete material list in the initial stage is inevitable.

Research on the energy performance of HVAC systems and GSHP systems is not considered as valid information because the energy system cannot be classified as a distinctive architectural decoration style feature. By the same token, studies of building reconstruction or retrofit that are absent from the design process are also considered to be inconsistent with the scope of this discussion.
Works that primarily focus on comparing and evaluating optimization algorithms, designing platforms, and frameworks, and simulation models are likewise excluded. It must be acknowledged that the validity and effectiveness of designing platforms and simulation experimental models is a significant research topic [14–16]. The research on the accuracy and robustness of energy-saving optimization algorithms is also an influential research direction.
1. Previous Reviews

Prior to this, energy-oriented building-optimized design has become a hot area. Related research work has been mentioned many times. R. Pacheco et al. [17] reviewed design criteria related to residential heating and cooling energy consumption. R. Evins [18] reviewed issues associated to the application of calculations to sustainable design and predicted research trends. V. Machairas et al. [19] investigated novel algorithms and demonstrated their characteristics and capabilities. V.S.K.V. Harish and A. Kumar [20] summarized and classified various important methods for building energy system modeling developed in recent years. T. Østergård et al. [21] studied the statistical methods based on energy consumption, proposed a decision framework, and explored the possibility of interaction between CAD software. X. Shi et al. [22] analyzed the core literature from the perspective of the architect to reveal the current status of energy-saving design of buildings, affirming that optimization design is a promising technology. K. Amasyali and N.M.E. Gohary [23] introduced the application scope, characteristics, and processing methods of building energy prediction models. X. Shi et al. [24] explained the specific reason for the gap between the performance figures predicted during the design phase and the actual results during the use phase. Z.C. Tian [25] conducted a questionnaire survey on potential obstacles to building energy efficiency simulation and optimization technology, and classified the general procedures of the technology based on the survey results.

In addition, there are some research materials that are indirectly related to this review. They focus on dynamic simulation models for sustainable building design [26,27], indispensable simulation tools in the design process [28–31], and computer-based optimization methods [32–34].

Performance-Oriented Design and Optimization

Sustainable building design means seeking a global balance between reducing nonrenewable energy sources and controlling negative environmental impacts. Extensive research has found that different types of building components and systems contribute to energy conservation and emissions reduction in multiple ways. Among these categories, the design variables (even colors) associated with building physics, specifically the building envelope, form, and shading system, affect both energy performance and indoor comfort to a considerable extent [34,35]. As a discipline between art and science, the knowledge gained by architecture in dealing with different climate background problems is not only a scientific achievement, but also a cultural metaphor in creative exploration [36]. Building envelopes, forms, and shading systems are elemental catalogues that enable an equation to be established between environmental factors and building graphics. These derivable components can drive static or dynamic changes in the building and are the syntax that forms the response mechanism between environmental conditions and design solutions [37].

1. Envelopes

Envelopes are the boundaries and barriers of internal and external spaces to block negative factors in the environment to meet lower energy consumption and comfort. Directly related to the energy optimization of the building envelope is the screening and sequencing of the design scheme and its thermal parameters. E. Ghisi and J. Tinker [38] minimized energy consumption by capturing the best window area using digital analysis techniques. M. Košira et al. [39] defined five simplified baseline models in central European climate conditions. The best energy-saving solution was found by dynamically searching the building’s window area and azimuth. L.W. Wen et al. [40] created a default value assignment map that served the early performance design phase and assisted architects to obtain effective simulation data of office buildings in various regions of Japan. M. Trebilcock et al. [41] examined school buildings in the representative climate of Chile and extracted important passive strategic design parameters. On the basis of a dynamic simulation of a multivariable combination classroom prototype, it was found that the glass area was not directly related to heating demand. V.Ž. Leskovar and M. Premrov [42] developed a measure for acquiring the energy efficiency of prefabricated wood-frame residential buildings using exterior walls with different thermal properties as independent variables and found that there is convergence between the window area and the annual energy demand. E.A. Krieteyer et al. [43] designed a dynamic glass curtain wall system. By switching the form of pixel array of movable louvers, visual visibility was maintained while reducing the invasion of sunlight. C. Hachem and M. Elsayed [44] conducted a comparative study on the innovative design of multistory office buildings. An irregular saw-tooth-shaped epidermal cell was designed. The overall performance was optimized by controlling the balance of cooling and heating loads. C. Hachem [45] designed a folded plate façade system based on previous concepts. The complex geometry shown did not affect the building but observed great potential in photovoltaic power generation applications. Digital software with friendly, customizable user interfaces is increasingly popular with architects and provides more possibilities for exploring parameterized building performance design [46]. A.X. Zhang et al. [47] took Chinese school buildings in cold climate as the research object and investigated multiple combinations between different spatial configurations and facade design parameters. The strength Pareto evolutionary algorithm was introduced to achieve a compromise between energy consumption and indoor comfort. A. Toutou et al. [48] explored the possibility of parametric modeling methods in the sustainable design of residential buildings. A variety of room fenestrations were tried, with EUI (energy use intensity) and SDA (spatial daylight autonomy) as evaluation indicators. The Pareto curve formed contained all the optimal genomes. K.B. Lauridsen and S. Petersen [49] explored a performance-based conceptual design paradigm for facades. The developed parametric information system automatically generated a series of curtain wall design solutions based on the expected standards of indoor climate, daylight, and energy performance defined by the technicians.

D.A. Chi et al. [50] designed a double curtain wall for office buildings. The perforated solar screens

placed were subjected to a matrix change of 16 different shapes and perforation rates, and the preferred solution balanced daylight availability and total annual energy consumption.

The effectiveness and efficiency of conventional scenario-by-scenario calculations and manual control of multiobjective optimization methods are limited by the breadth of design and the professional skills of the participants [51]. Advances in artificial intelligence technology have opened up new possibilities in the design process and the way of interaction. With the help of genetic algorithm, researchers have carried out a series of design experiments on the optimal size of building windows under specific climate conditions to improve the building’s energy efficiency [52,53]. K. Negendahl and T.R. Nielsen [54] presented a holistic folded facade design process. The self-shading mechanism generated by the folding amplitude of the facade module regulated multiple optimization objectives.

J. Wright and M. Mourshed [55] described a window element design that divided the building envelope into rectangular grids. The adopted algorithm constrained the design variables of the window, which could effectively improve energy saving. E.J. Glassman and C. Reinhart [56] showed innovative ways to customize building components or shapes by defining the building elements on the

exterior wall as variables. Linked simulation tools and algorithm technologies increased the design’s climate adaptability. N. Delgarm et al. [57] proposed a design measure based on artificial bee colony (ABC) algorithm. The introduced optimization program helped expand a large number of feasible configurations regarding the glass area and the room rotation angle. G. Rapone and O. Saro [58] modeled a typical facade of an office building and based on the dynamic simulation engine and the PSO algorithm, the design parameters that affect the thermal performance such as glass percentage were searched automatically. The optimized design results of each façade are discussed.

It must be recognized that in the actual optimization problem, there is a certain probability of mismatch between the targets. When a set of uniquely designed concepts are no longer applicable, a more robust multi-criteria optimization decision is needed [59]. C. E. Ochoa et al. [60] proposed an acceptance threshold to avoid conflicts between building energy and visual dynamics. The introduced solution space avoided the contradiction caused by the single target search method (window size). J.F. Grygierek and K. Grygierek [61] analyzed the life cycle cost optimization of single-family homes under changing climate conditions. Genetic algorithm was applied to explore many variables such as building orientation, types and sizes of windows, as well as the sum of window areas and was committed to building cooling and heating costs convergence. Y.N. Zhai et al. [62] tried a multiobjective optimization method to help designers choose the best window design solution. All the solutions provided in the pareto boundary diagram clearly demonstrated its performance. B. Lartigue et al. [63] introduced an evolutionary algorithm for optimizing the window-to-wall area ratio. The window parameters provide coordinated the holistic performance of multiple targets simultaneously. B.J. Futrell et al. [64] used a hybrid algorithm to optimize the daylighting and heat gain in the four directions of a classroom. Pareto efficient solutions helped decision makers with respect to tradeoffs between different orientations and design objectives. N. Delgarm et al. [65] applied a prototype model in the Middle East to verify the interaction between building parameters represented by window size and energy cost. The result of multistandard optimization was compared with those of single ones, and the effectiveness of the method was proven. S.Q. Gou et al. [66] employed the Monte Carlo method (MCA) to analyze the global sensitivity of input variables of apartment prototype in two climate zones in China. The artificial neural network (ANN) and nondominant sorting genetic algorithm (NSGA-II) were embedded in the passive strategy to control indoor temperature and expenditure during operation. T.M. Echenagucia et al. [67] investigated the number, location, and shape of windows in an open office building in multiple urban contexts. Refine search was performed for all cases. The analysis results demonstrated the delicate relationship between window layout and energy efficiency. A. Hani and T.A. Koiv [68] used a hybrid multidimensional optimization algorithm (GPSPSOCCHJ) in the optimization calculation of a modern office building. Quick selection maps of different elevation design schemes and annual total unit energy consumption were compiled. M. Khatami et al. [69] used the same technique in the optimization of glass curtain walls. M. Ferrara et al. [70] aimed at the optimization of individual classrooms. The selected particle swarm optimization algorithm (PSO) was configured with the geometric and material parameters involved and coordinated solutions ensured indoor environment and energy requirements.

S. Carlucci and L. Pagliano [71,72] proposed a particle swarm optimization algorithm to improve the

thermal comfort of net-zero energy buildings. The design strategy provided by the adaptive comfort model optimized the window area percentage and the glass unit. Subsequently, a new optimization study was conducted for indoor visual comfort. W. Yu et al. [73] described multiobjective optimization of a typical built environmental performance. The improved algorithm helped the multiple constraint variables of envelope design to be extensively weighed on several conflicting criteria.

The complexity and uncertainty of a large search space are inevitable problems in the integrated simulation and optimization of building energy consumption. The comparison and coordination of various algorithms is a universal solution [74]. K. Bamdada et al. [75] adopted a multiparameter combination strategy to solve the problems of typical commercial construction in four different climatic conditions in Australia. Compared with the three benchmark algorithms, the applied ant colony algorithm showed superiority. The selected aspect ratio of the window took into account the illumination

level and energy efficiency. B.H. Si et al. [76] studied the influence of the shape of the compound slope roof on the building performance based on a new complex building. Mathematical description methods could not fully address the complexity presented by real-world building functions and forms. A high-precision surrogate model was developed and the effectiveness of four multiobjective algorithms was evaluated. A.T. Nguyen and S. Reiter [77] reported on the systematic global optimization design of low-cost housing in developing countries. Particle swarm optimization (PSO) and Hooke–Jeeves algorithm were combined to simulate and calculate a series of variables such as the square angle and window details. The results showed that there were many differences between naturally ventilated rooms and air-conditioned homes. P. IHM and M. Krati [78] optimized the envelope design of a single family villa in Tunisia. Combined with sequential search and violent search technology, the design features of residential buildings in specific locations were proposed. Compared with current residential design practices, annual energy cost was reduced by 50%.

Envelope components are a major part of the building system. It is a vital element that verifies the indoor environmental quality, energy consumption, and architectural aesthetics [79]. Statistics have found that papers on building envelopes accounted for about 37% of all shares. The imaginative envelope design ensures the strong individual character of the building. Seventy percent of the work in the screened literature revolves around window configurations and building skin configurations (Table 1). Design variables include window form, location, window-to-wall ratio, aspect ratio, and curtain wall unit. The design of windows and curtain walls strongly depends on the architect’s design awareness and the needs of the client. It can create a stylized architectural appearance and provide occupants with an attractive view. At the same time, their impact on the physical environment of the building has been widely recognized and given due attention. In contrast, some papers involve overhang thermophysical parameter descriptions. In order to avoid personal subjective judgment, it is classified as a holistic façade design study. One of the case studies on roof system is equally important [76].

Table 1. Literatures focused on design and optimization of building envelopes.

Algorithm

Ref Date Author(s) Background Design Proposals Category Building Type Design Practice Approach Modeling Tools Platform/Plug-in Simulation Tools Optimization

P.K.B. Lauridsen and

[49] 2014

S. Petersen

Non-architectural+2 Energy Performance

Building skin configurations

Non-residential building

Detailed and aesthetics

Theoretical & Simulation

Rhino

Grasshopper DIVA

ICEbear Galapagos

BuildingCalc IDA-ICE

Genetic algorithm

L.G. Caldas and

Visual Performance

[52] 2002

L.K. Norford

Non-architectural+2 Energy Performance Window

Non-residential building

Simplified and fictitious

Theoretical & Simulation

- AutoLisp DOE-2.1E Genetic algorithm

Energy Performance

configurations

[62] 2019 Y. N. Zhai et al. Non-architectural+4 Thermal Performance

Visual Performance

Window configurations

Non-residential building

Simplified and fictitious

Theoretical & Simulation

- Matlab EnergyPlus NSGA-II

[41] 2016 M. Trebilcock et al. Architectural+2

Non-architectural 2

[40] 2017 L.W. Wen et al. Architectural+1

Non-architectural 2

Energy Performance Window

configurations

Non-residential building

Simplified and fictitious

Field investigation & Simulation

Theoretical & Simulation

GenOpt EnergyPlus -
- EnergyPlus Tailor-made

[64] 2015 B.J. Futrell et al. Architectural+2

Non-architectural 1

Energy Performance Visual Performance

Energy Performance

Window configurations

Non-residential building

Simplified and fictitious

Theoretical & Simulation

EnergyPlus Radiance

Grasshopper

- GenOpt

Hooke Jeeves & PSO algorithm

K. Negendahl and

[54] 2015

T. R. Nielsen

Non-architectural+2

Visual Performance Thermal Performance LCC Performance

Building skin configurations

Non-residential building

Detailed and aesthetics

Experimental & Simulation

Rhino

Ladybug & Honeybee Termite Octopus

EnergyPlus Radiance

SPEA-2

[50] 2018 D. A. Chi et al. Architectural+3 Energy Performance

Visual Performance

Building skin configurations

Non-residential building

Simplified and fictitious

Theoretical & Simulation

Grasshopper DIVA Archsim

EnergyPlus Radiance

[78] 2012 P. Ihm and M. Krarti Non-architectural+2 Energy Performance

Rhino

LCC Performance

Window configurations

Residential building Simplified and

Theoretical & Simulation

Sequential search (SS) Brute-force

[43] 2011 E. A. Krietemeyer et

- - DOE-2

al.

Energy Performance Visual Performance

Architectural+3

Building skin configurations

Non-residential building

Simplified and fictitious

fictitious

Theoretical & Simulation

eQuest

Ecotect - Radiance - OPTICS

[55] 2009 J. Wright and M.

- No case

[39]	2018	Mourshed M. Košira et al.	configurations Non-architectural+3 Energy Performance Window
[76]	2019	B. H. Si et al.	Architectural+6 Energy Performance Roof configurations Non-re
[63]	2013	B. Lartigue et al.	Non-architectural+3 Energy Performance Window Non-re
[65]	2016	N. Delgarm et al.	Non-architectural+4 Energy Performance Window Non-re
[45]	2018	C. Hachem	Architectural+1 Energy Performance Building skin Residenti LCC Performance configurations

Non-architectural+2 Energy Performance Window

Non-residential building

Simplified and fictitious

Theoretical & Simulation

- EnergyPlus Genetic algorithm

configurations

OpenStudio

Theoretical & Simulation

- - EnergyPlus - NSGA-II

Thermal Performance

sidential

building

Detailed and

aesthetics

Experimental &

Simulation

SketchUp modeFRONTIER EnergyPlus

MOPSO MOSA

Evolution strategy (ES)

algorithm

Visual Performance

configurations

sidential building

sidential

Simplified and fictitious

Simplified and

Theoretical & Simulation

Theoretical &

- GenOpt TRNSYS Daysim Evolutionary

configurations

building

fictitious

Simulation SketchUp Matlab jEPlus EnergyPlus MOPSO

Detailed and aesthetics

al building

configurations

Theoretical & - - EnergyPlus

[58] 2012 G. Rapone and O.

Simulation

Saro

Non-architectural+2 Energy Performance Façade

Non-residential building

Simplified and fictitious

Theoretical & Simulation

Particle swarm optimization (PSO)

[38] 2001 E. Ghisi and J. Tinker Non-architectural+2 Energy Performance Window

configurations

Non-residential building

Simplified and fictitious

Theoretical & Simulation

- VisualDOE -

- GenOpt EnergyPlus

Energy Performance

[47] 2017 A. X. Zhang et al. Architectural+6 Visual Performance

Thermal Performance

Façade configurations

Non-residential building

Simplified and fictitious

Theoretical & Simulation

Grasshopper

Rhino Ladybug & Honeybee Octopus

EnergyPlus Radiance

SPEA-2

[53] 2016 Q. S. Ma and H.

Fukuda

Energy Performance Visual Performance

Architectural+2

Window configurations

Non-residential building

Simplified and fictitious

Theoretical & Simulation

Grasshopper

Rhino Ladybug & Honeybee Galapagos

EnergyPlus Radiance

Genetic algorithm

Table 1. Cont.

Non-architectural+4

Thermal Performance

configurations

fictitious

Simulation

jEPlus

Algorithm

neural network

Ref	Date	Author(s) Background Design Proposals Category Building Type Design Practice Approach Modeling Tools Platform/Plug-in Simulation Tools Optimization
[66]	2018	S. Q. Gou et al. Architectural+1 Energy Performance Façade Residential building Simplified and Theoretical & SketchUp Matlab SimLab EnergyPlus NSGA-II Artificial
[44] [67]	2016 2015	(ANN) C. Hachem and M. Non-architectural+2 Energy Performance Building skin Non-residential Detailed and Theoretical & Rhino Grasshopper EnergyPlus - T. M. Echenagucia et Architectural+2 Energy Performance Window Non-residential Simplified and Theoretical & - - EnergyPlus NSGA-II
		al.	Non-architectural+2 configurations building fictitious Simulation
[48]	2018	A. Toutou et al.	Non-architectural 3 Energy Performance Façade Residential building Simplified and Theoretical & Rhino Grasshopper EnergyPlus SPEA-2 Honeybee Octopus
[60]	2012	C. E. Ochoa et al.	Non-architectural+4 Energy Performance Façade Non-residential Simplified and Theoretical & - - EnergyPlus -
[61]	2017	J. F. Grygierek and K.	Energy Performance Non-architectural 2 Window Residential building Simplified and Theoretical & - Matlab EnergyPlus Genetic Algorithm
		Grygierek	Thermal Performance
[75]	2017	K. Bamdada et al.	Ant colony algorithm Non-architectural 4 Energy Performance Façade Non-residential Simplified and Theoretical & - Matlab EnergyPlus Nelder-Mead hybrid PSO-HJ algorithm
[57]	2016	N. Delgarm et al.	Non-architectural+4 Energy Performance Façade Non-residential Simplified and Theoretical & SketchUp Matlab EnergyPlus Artificial bee colony
[72]	2015	S. Carlucci et al.	Energy Performance Window Simplified and Theoretical & Non-architectural 4 Residential building SketchUp GenOpt EnergyPlus NSGA-II Thermal Performance
[69]	2014	M. Khatami et al.	Non-architectural+4 Energy Performance Window Non-residential Simplified and Theoretical & - GenOpt EnergyPlus GPSPSOCCHJ
[42]	2011 V. Ž. Leskovar and M. Non-architectural 2 Energy Performance Window Residential building Simplified and Theoretical & - - Passive House - (PHPP)
[68]	2012 A. Hani and T. A. Non-architectural+2 Energy Performance Window Non-residential Simplified and Theoretical & - GenOpt IDA-ICE GPSPSOCCHJ
[77]	A. T. Nguyen and S. Energy Performance Façade Simplified and Theoretical & Particle Swarm 2012 Reiter Non-architectural+2 LCC Performance configurations Residential building fictitious Simulation - GenOpt EnergyPlus Optimization (PSO)

Elsayed

configurations

building

aesthetics

Simulation

Ladybug&Honeybee

+ Visual Performance

configurations

fictitious

Simulation

Ladybug &

Radiance Daysim

Visual Performance

+ LCC Performance

configurations

building

fictitious

Simulation

+ configurations

building

fictitious

Simulation

GenOpt

algorithm (NM)

Thermal Performance

+ Visual Performance

configurations

building

fictitious

Simulation

jEPlus

(ABC)

Premrov +

configurations

building

fictitious

Simulation

Planning Package

algorithm

Koiv

configurations

building

fictitious

Simulation

algorithm

Thermal Performance Energy Performance

[70] 2015 M. Ferrara et al. Non-architectural+4 Thermal Performance

Visual Performance

Façade configurations

Non-residential building

Simplified and fictitious

Theoretical & Simulation

Ecotect GenOpt TRNSYS

Hooke–Jeeves algorithm

Particle swarm optimization (PSO)

S. Carlucci and

Daysim

L. Pagliano

Thermal Performance

configurations

fictitious

Simulation optimization (PSO)

E. J. Glassman and

Architectural+1

Energy Performance

Façade

Non-residential

Simplified and

Theoretical & Rhino Grasshopper EnergyPlus Genetic algorithm

C. Reinhart

Non-architectural+1

LCC Performance

configurations

building

fictitious

Simulation Galapogos

DIVA

[71]	2013
[56]	2013
[73]	2015

Non-architectural+2 Energy Performance

Window

Residential building Simplified and

Theoretical &

SketchUp GenOpt EnergyPlus Particle swarm

W. Yu et al. Non-architectural+5 Energy Performance

Thermal Performance

Window configurations

Residential building Simplified and

Theoretical & Simulation

- Matlab EnergyPlus

NSGA-II

Genetic algorithm Artificial neural network (ANN)

fictitious

1. Form

Architectural form design is considered one of the most challenging tasks. Visualized and lifesome architecture always sparks discussion. In a sustainable context, building form is given more possibilities. A large number of strategies for optimizing and generating complex shapes have been found in the published data. The essence of finding a moderate architectural form is the process of solving a function because the solution set space can be nonlinear. M.S. Al-Homoud [80] mentioned a thermal optimization design method that combined energy simulation with direct search technology. It helped to quantify the shape characteristics of buildings under different climatic conditions (total building area, building height, and aspect ratio). W.S.S.W.M. Rashdi and M.R. Embi [81] found that the architectural form was a double-edged sword. Loose shape was not conducive to reducing solar radiation but had a higher cooling load. N.C. Brown and C.T. Mueller [82] discussed the research results of long-span building typology and based on finite element structure modeling and building energy consumption simulation, a variety of high-performance building form solutions were sought.

In areas with abundant natural resources, the connection between the architectural form and the microclimate environment has received increasing attention. Architects and users also appreciate integrated design (rather than support frames) to enhance the ornamental nature of the building.

J. Shaeri et al. [83] conducted research on prototypes of multistory office buildings in three cities. The collision between the building extension and the dominant wind direction in the city was analyzed. With reference to statistics, the most suitable building shape under different climate conditions was determined. A.M.A. Youssef et al. [84] explored the feasibility of photovoltaic integration in large buildings. The design characteristics of multiple groups of building shapes were tested and evaluated in the study. The improved solution achieved the best photovoltaic utilization and reduced net energy consumption. C. Waibel et al. [85] implemented multiobjective optimization in a microclimate design workflow coupled with multiple simulations. In this study, the roof and facade of four office buildings were used as modeling variables and captured the essential dependencies between maximum renewable energy potential and minimum operating costs. I.G. Capeluto [86] introduced a concept design called solar energy collection envelope. The generated building shape made it possible to implement self-shading in a specific required time. Two completed works have carried out design studies on the energy efficiency of geometric attributes and layout plans and the direct relationship between natural energy use and candidate options has been systematically explored [87,88].

The complexity of the optimization objective and the range of influence of the design variables determine the simulation run time. Sensitivity analysis is an effective preprocessing method that has been used to help identify decision variables and squeeze value ranges. T.L. Hemsath and K.A. Bandhosseini [89] explored the best way to filter the design properties of geometric parameters. Through the embedded sensitivity analysis, the statistical evaluation of the aspect ratio, the number of layers, and the shape were completed, and the important factors in the context of energy consumption were determined. The building shape was generated by genetic algorithm. D.T. Dubrow and M. Krarti [90] developed a procedure for selecting geometrical parameters of American benchmark residential buildings. For the simulation results of shape options, optimization tools arranged them in ascending order of fitness value. The results showed that the shape coefficients of two types of buildings always had energy saving potential under five different climatic conditions. S. Asadi et al. [91] demonstrated the selection and definition of independent variables affecting building energy consumption using Monte Carlo models. Multiple linear regression analysis was performed on design variables such as different contours, number of layers, and height. The calculation results were used in the optimization process to evaluate the energy cost of commercial buildings.

Multidisciplinary design teams often face complex data editing and transmission. In this case, the timeliness of the optimization method cannot be demonstrated, but also the potential risk of optimization failure is increased. The sophisticated multiobjective optimization framework can make more efficient design decisions according to the stated objectives and use simulation tools more purposefully in the optimization process. K.W. Chen et al. [92] constructed a design framework that

integrated and optimized the shape of building and courtyard with cooling loads while ensuring daylight. The compromised design model took advantage of a statistical technique called prototype analysis to expand the possibilities of design. Z.W. Li et al. [93] realized energy efficiency optimization of representative building forms through a bidirectional workflow. Through rapid feedback indicators, the solution maintained the maximum proportion of functional areas and improved the natural lighting of the building without increasing energy costs. E. Lin and D. J. Gerber [94,95] initiated a design experiment on public buildings. The developed design model quickly collected quantitative and qualitative data and generated complex geometric shapes and analyzed their visualization effects, provided a building solution with better fitting performance. K. Konis et al. [96] developed a simulation-based passive architecture design framework based on the multi-engine parallel computing and visualization capabilities provided by the parametric tool platform. The form-finding component revealed the relationship between the building form combined with the floor plan and the inner courtyard and the energy use intensity.

On the basis of the powerful graphics processing and language programming ability of the computer, a variety of independently developed architectural shape design theories that expand or even surpass Euclidean geometry are another the important discoveries in this review. V. Granadeiro et al. [97] creatively developed a generative theory called “shape grammar”. The computer coding design system established a direct connection between energy simulation and parametric design through complex grammatical transformations. The diversity of design was explored on the premise of ensuring the objective principle. L.G. Caldas [98,99] conducted several design experiments on the work of the famous architect Alvaro Siza. The genetic algorithm was used as search engines to combine cost and energy evaluation criteria to generate shapes and execute design intent. Y.K. Yi and A.M. Malkawi [100] developed a novel approach to define form by controlling the hierarchical connection between building agent nodes. Syntax-based patterns overcame the limitations of common target control variables and integrated building performance optimization more smoothly into the design process. J.T. Jin and J.W. Jeong [101] proposed a design theory for free-form buildings. Parametric modeling tools were used to divide the surface of the building into finite elements. The heat gain and loss of buildings were estimated by genetic algorithm, so as to optimize energy consumption.

Agirbas [102] conducted an energy efficiency analysis study on complex geometries. The design platform allowed forms to flow freely based on environmental interference factors. The generated conceptual design scheme had good performance in many indicators such as lighting, radiation, and building area. Furthermore, tailor-made computer design tools have been introduced in multiple studies. Interactive visualization platforms have helped architects explore different physical geometries and weighed the solution’s energy, heat, and visual performance [103–105].

Twenty-six of the documents reviewed were involved in the optimization of the architectural form. With reference to all publications, among the mentioned design elements, scale factors (aspect ratio, surface volume ratio, etc.) of standard floor plans played an important role in the energy-saving optimization process related to building form (Table 2), covering almost all the ways in which physical design affects building energy consumption. Correspondingly, the shape and standard layer design information also belongs to the research dimension of architectural visual art. This novel design concept drives the dialogue between shape parameters and physical phenomena. Architectural forms can be regarded as a captured energy flow, which is expressed in the form of metaphors and analogies with the help of natural ecosystem mechanisms [106].

Table 2. Literatures focused on design and optimization of building form.

Algorithm

Ref Date Author(s) Background Design Proposals Category Building Type Design Practice Approach Modeling Tools Platform/Plug-in Simulation Tools Optimization

1. S. S. W. M. Rashdi

[81] 2016

Architectural 2 Energy Performance Shape e ect Non-residential

Simplified and

Theoretical &

Revit - Autodesk Ecotect -

and +

M. R. Embi

ff building

fictious

Simulation

[89] 2015 T. L. Hemsath and K.

Architectural 2 Energy Performance Shape e ect Non-residential

Simplified and

Theoretical &

Grasshopper

Rhino

		A. Bandhosseini + ff building fictious Simulation Galapagos
[97]	2015	V. Granadeiro et al. Architectural+1 Energy Performance Shape effect Residential building Simplified and Theoretical & - Matlab EnergyPlus Tailor-made
[85]	2019	Grasshopper Energy Performance Non-residential Simplified and Theoretical & Radial Basis Function
		Honeybee (RBFOpt)
[82]	2016 N. C. Brown and Architectural 2 Energy Performance Shape e ect Non-residential Simplified and Theoretical & Rhino Grasshopper EnergyPlus NSGA-II LCC Performance
[99]	2008 L. G. Caldas Non-architectural+1 Energy Performance Shape effect Non-residential Detailed and Experimental & - GENE_ARCH DOE-2.1E Genetic algorithm
[90]	2010 D. T. Dubrow and Non-architectural+2 Energy Performance Shape effect Residential building Simplified and Theoretical & - Matlab DOE-2 Genetic algorithm
[100]	2009	Y. K. Yi and A. M. Malkawi	Architectural+2	Energy Performance	Shape effect Non-residential Simplified and Theoretical & Rhino Grasshopper EnergyPlus Genetic algorithm
[86]	2003	I. G. Capeluto	Architectural+1	Energy Performance	Shape effect Non-residential Simplified and Theoretical & ENERGY - Tailor-made Tailor-made Shape
[104]	1997	W. Marks	Non-architectural+1	Energy Performance	Shape effect Non-residential Simplified and Theoretical & CAMOS - Tailor-made Tailor-made building fictitious Simulation

DIVA

EnergyPlus Genetic algorithm

Non-architectural+3

- 1. Waibel et al. Non-architectural*3

LCC Performance Shape effect

building

fictious

Simulation

Simulation Rhino

Ladybug & Opossum

EnergyPlus

Shape grammar

Optimization

- - 1. T. Mueller

+ Structural Performance ff

building

fictious

Simulation

Karamba Archsim

building

aesthetics

Simulation

Shape grammar

M. Krarti

LCC Performance

fictitious

Simulation

building

fictitious

Simulation

building

fictitious

Simulation

grammar

[92] 2018 K. W. Chen et al. Architectural+2 Energy Performance Shape effect Non-residential

Non-architectural+1 building fictitious Simulation							eQUEST

[103] 2015 Z. X. Conti et al. Non-architectural 3 Energy Performance Shape e ect Residential building Simplified and Experimental & Tailor-made							Viewer	NSGA-II
							Tailor-made
[91] 2014 S. Asadi et al. Non-architectural+3 Energy Performance Shape effect Non-residential Simplified and Theoretical & -							- eQUEST DOE-2	-
[88]	2010 J. H. Kämpf and Non-architectural+2 Energy Performance Shape effect Non-residential Simplified and Theoretical & Tailor-made - Radiance Evolutionary
[105]	2002 H. Jedrzejuk and Non-architectural+1 Energy Performance Shape effect - No case Theoretical & CAMOS - Tailor-made Tailor-made
[101]	2014 J. T. Jin and J. W. Non-architectural+2 Energy Performance Shape effect - No case Theoretical & Rhino Grasshopper EnergyPlus Genetic algorithm
[96]	2016	K. Konis et al.	Architectural 3 Energy Performance Shape e ect Non-residential Simplified and Theoretical & Rhino Grasshopper EnergyPlus SPEA-2
[102]	2018	A. Agirbas	Honeybee Octopus Architectural 1 Energy Performance Shape e ect Non-residential Simplified and Theoretical & Rhino Grasshopper EnergyPlus					SPEA-2
				Visual Performance		Honeybee Octopus Radiance Daysim
[87]	2019	V. J. L. Gan et al.	Non-architectural+6	Energy Performance	Shape effect	Residential building Detailed and Theoretical & - Matlab DOE-2 Genetic algorithm aesthetics Simulation

Simplified and

Theoretical &

- Radiance

Lightsolve

NSGA-II

+ Visual Performance ff

fictitious

Simulation

building

fictitious

Simulation

- - 1. Robinson

building

fictitious

Simulation

Tailor-made

algorithm

W. Marks Simulation

Jeong

+ Visual Performance

ff building

fictitious

Simulation

Galapagos Ladybug &

Radiance

+ ff

building

fictitious

Simulation

Ladybug &

OpenStudio

Table 2. Cont.

Algorithm

Ref Date Author(s) Background Design Proposals Category Building Type Design Practice Approach Modeling Tools Platform/Plug-in Simulation Tools Optimization

[83] 2019 J. Shaeri et al. Architectural+2

Non-architectural 2

Energy Performance Shape effect Non-residential

Simplified and fictitious

Theoretical & Simulation

DesignBuilder Radiance

[98] 2005 L. G. Caldas Non-architectural+1 Energy Performance Shape effect Non-residential

building

- -

building

Detailed and aesthetics

Theoretical & Simulation

- DOE-2.1E Genetic algorithm

[93] 2018 Z. W. Li et al. Architectural+4 Energy Performance

Visual Performance

[84] 2016 A. M. A. Youssef et al. Non-architectural+3 Energy Performance

LCC Performance

Shape effect Non-residential

building

Simplified and fictitious

Theoretical & Simulation

Matlab DesignBuilder Genetic algorithm
GenOpt DOE-2 Genetic algorithm

[80] 2005 M. S. Al-Homoud Non-architectural+1 Energy Performance Shape effect - No case Theoretical &

Simulation

LCC Performance

Revit

Matlab modeFRONTIER

ENERCALC Direct search

2014 S. H. E. Lin and D. J.

Gerber

2014 D. J. Gerber and S. H.

E. Lin

Architectural+2 Energy Performance Architectural+2 Energy Performance

Shape effect Non-residential

LCC Performance

building

Detailed and aesthetics

Theoretical & Simulation

Microsoft Excel Matlab

Green Building Studio

Genetic algorithm Genetic algorithm

1. Shading Systems

As part of the building envelope, the shading systems is designed to avoid unwanted daylight from causing undesired indoor temperature and lighting environment and reduce the additional operating costs of the building system. In order to meet a user’s preferences, the shading system enhances the building’s identity and reflects personal design capabilities. Therefore, this review lists them separately. Most of the commonly used shading devices are regarded as a passive measure. The basic principle is that no additional electrical equipment is required. L. Bellia et al. [107] analyzed the influence of the geometric characteristics of outdoor shading on the energy cost of typical office buildings in Italy. Simplified design standards were provided for engineers and architects. J.T. Kim and G. Kim [108] designed a combined external shading device and produced a miniature scale model. Experiments and simulations proved that it could effectively minimize the adverse effect of direct sunlight on the cooling load and improved the uniformity of illumination. H.H. Alzoubi and A.H. AlZoubi [109] performed a simulation comparison of three different shading designs. The illumination and energy consumption of the device were evaluated through field investigation. F. Mazzichi and

M. Manzan [110] showed the interference of different types of shading on heating, cooling, and lighting equipment during office building occupation. The analysis indicated that the mixed device composed of fixed overhangs and shutters had an obvious effect. A.K. Furundžic´ et al. [111] simulated different exterior shading conceptual models of a typical office building. A comparative analysis of multiple scenarios showed that shading facilities not only affect building energy efficiency, but also participate in carbon emissions. A. Sherif et al. [112,113], inspired by elements of vernacular architecture, invented a wooden perforated shading system. By controlling the rotation angle of the solar screen, the desired energy saving goal was achieved and the glare was significantly reduced.

M.C. Ho et al. [114] conducted field tests on the indoor illuminance of a typical classroom under various conditions. After verifying the effectiveness of the simulation scheme, a number of different combinations of shading designs were considered for candidates that demonstrated promising energy performance. A.K.K. Lau et al. [115] carried out targeted research on the shading system of high-rise buildings with large glass curtain walls. The simulation data of 20 sets of different facade orientations and different types of devices were compared. The results of the analysis showed that the application of shading devices was urgently needed on the east and west facades. It also clarified that the shading device had more energy-saving prospects than high-performance glass. F. Yassine and B.A. Hijleh [116] examined the energy-saving potential of horizontal overhangs, vertical fins, horizontal louvers, and vertical louvers and found that the parameters of rotation angle and protrusion length were more important than the orientation of the facade. A. Ghosh and S. Neogi [117] summarized the interference of building geometric elements on energy consumption, and tailored an external shading system. The applicability of the new equipment was verified through three different climatic data. F.F. Hernández et al. [118] evaluated the use of shading devices for high-light transmission office buildings in Mediterranean cities. Studies have shown that shading devices are an effective solution to the energy consumption and visual comfort of local buildings. But the type of glass and the weather parameters cannot be ignored. S. Liu et al. [119] suggested using shading devices on the opaque facades of public rental housing in Hong Kong. Different configurations such as the length, number, and tilt angle of the light shielding plate were discussed, and the maximum interference with energy efficiency was achieved. M. Alshayeb et al. [120] drafted three energy-saving measures for an upcoming medical center. Calculations showed that a 1:1 window shadow ratio dramatically reduced the cooling load.

Considering the interaction of all variables in a decision and scanning the predefined range

of each parameter line-by-line is a daunting task. A multi-benchmark parallel operation based on a parameterized information platform has been a recommended method. C. Kasinalis et al. [121] studied the correlation between the surface area of dynamic shading and the window-to-wall ratio in terms of energy saving potential. D.R. Ossen et al. [122] performed a parametric study on various performance variables of external overhang. The relationship between cooling load and daylight level, as well as the geometry of the horizontal shading, were analyzed. A. Wagdy and F. Fathy [123]

introduced an exhaustive search method for shading systems. Convergent solutions selected with a combination of multiple design variables such as window-to-wall ratio, number of shutters, and shutter angle reflected the general trend of building performance. M.V. Nielsen et al. [124] investigated various types of shading using cooling, heating, and lighting as metrics. The results showed that dynamic shading performed the best in total energy consumption and sunlight environment tests and the optimal solution revealed a high degree of dependence between design variables. A. Eltaweel and Y. Su [125,126] designed an automated venetian blinds for office buildings and controlled the length, width, interval, and specific rotation angle of each slat based on parameterized means. Specific dates (spring breeze, summer solstice, and east solstice) were selected to verify the conditions of the shading element. It was proven that the system was superior to traditional shutters considering energy reducing and indoor comfort. J. González and F. Fiorito [127] solved the external shadow optimization of typical office space with the same strategy. The energy efficiency and emissions of alternatives were recognized as better than industry standards.

In the meantime, in order to effectively access complex targets, it has been found that a large number of studies have coupled evolutionary algorithms with building energy optimization programs. To date, this has be called a mainstream technology trend. H. Sghiouri et al. [128] studied the control parameters of fixed shading of external windows in three climate cities. Through the use of genetic algorithms, the performance optimization of different regions and orientations was completed. Evidence showed that there was no contradiction between improving environmental quality and regulating energy saving. M. Manzan and F. Pinto [129,130] studied the energy consumption of external shading in different climatic conditions. The coupled multiobjective algorithm referenced configuration variables such as rotation angle, overhang length, and glass system and a correlation scheme was generated. A. Kirimtat et al. [131] designed a novel amorphous shading apparatus and constrained the design solution with two mutually interfering goals (total energy consumption and effective daylight). The applicability of the two algorithms was compared based on performance evaluation criteria. M. Khoroshiltseva et al. [132] designed fixed shading instruments for the exterior walls of residential buildings. A Harmony Search-based design derivation method was used to reduce the operation cost while maintaining the lighting comfort. The effectiveness of the shading was characterized by the analysis data. Additionally, according to observations, the user-friendly and customized collaborative optimization module solved conflicting design goals in a reasonable running time [133,134].

With the introduction of kinetic motion, contemporary architecture has developed to a stage of self-adaptation and response. Building have the ability to physically reconfigure themselves to accommodate the variability of location or geometry [135]. E.T. Cachat et al. [136] proposed a photovoltaic integrated dynamic shading device (PVDS) design scheme. The facility overcame the limitations of standard shading systems and showed advantages by improving illumination and energy consumption. It also emphasized that the optimization plan could not meet three goals at the same time, and special tradeoffs were needed. M.M.S. Ahmed et al. [137] reported the experimental results of an intelligent dynamic shading system applied to residential buildings. The system consisted of a vertical movable frame and a horizontal turnover plate, which were fixed on a window facing south. Room temperature and energy consumption were recorded separately. The measured data indicated that the device could effectively reduce cooling energy consumption. S. Adriaenssens et al. [138] designed a shape-shifting modular shading system based on dialectic form finding. The purpose of restraining solar radiation was achieved by adjusting the degree of bending and complex motion of the controlled flexible shell. Experiments exhibited that the shading module could effectively reduce the annual load. L. Giovannini et al. [139] developed an adaptive shape variable shading system for arid climates. By controlling the opening and closing mechanism and the porosity of the polygon module, the purpose of energy saving was achieved. Analysis shown that the device was more efficient than external shutters and reflective glass. M. Manzana and R. Padovana [140] designed a hybrid shading system based on changes in the solar position in summer and winter. Fixed shading and real-time activated blinds provided a new solution for energy savings. In subsequent studies, the control

system was optimized in more detail based on the occupancy schedule, thereby minimizing shutter activation [141]. M. Pesenti et al. [142] invented a microactuator adaptive shading device. The opacity of the origami shading system was controlled by overlapping folds and angle changes. The contribution of the equipment to reducing energy consumption and indoor daylight distribution was highlighted.

Z. Nagy et al. [143] introduced a dynamic self-shading system constructed from a lightweight frame and a rectangular photovoltaic module. PV panels driven by pneumatic actuators resisted unwanted light while tracking the sun’s position. The simulation results showed the superiority of energy saving and solar power generation.

All the reviewed texts (36 copies) pointed out that the classification of shading systems is closely related to the types of natural resources, climate attributes, solar azimuth, and other environmental factors involved [144]. Similar to the other elements in the building, a cantilever exposed to the microclimate, shutters, and various prefabricated adjustable modules form the design language of the shading system (Table 3). A fixed shading device shows the advantages of easy installation and cost controllability. Therefore, the application research in this field is widely distributed in this survey, accounting for 68% of the total. However, its cross-seasonal energy performance has limitations [145]. With the rapid development of adaptive building theory and mechanical dynamics knowledge, movable shading devices are gradually popularized. Accordingly, complex construction and excessive installation and maintenance costs has become obstacles to further promotion. The adaptability and sustainability demonstrated by the hybrid system provide designers with more choices, which is a promising research direction.

Table 3. Literatures focused on design and optimization of shading system.

Algorithm

Ref Date Author(s) Background Design Proposals Category Building Type Design Practice Approach Modeling Tools Platform/Plug-in Simulation Tools Optimization

[132] 2016 M. Khoroshiltseva et

al.

Non-architectural+3 Energy Performance

Fixed Shading Residential building Simplified and

Theoretical & Simulation

SketchUp - EnergyPlus Harmony Search

[108] 2010 J. T. Kim and G. Kim Architectural+2 Energy Performance

Thermal Performance

fictitious

Visual Performance

Fixed Shading Residential building Detailed and

Experimental & Simulation

Revit Architecture Revit MEP

- IES-VE Radiance -

Algorithms

H. H. Alzoubi and A.

[109] 2010

aesthetics

H. AlZoubi

[110] 2013 F. Mazzichi and

M. Manzan

Energy Performance Visual Performance

Non-architectural+2 Energy Performance

Architectural+2

Visual Performance

[111]	2019	A. K. Furundžic´ et al. Architectural+2 Energy Performance Fixed Shading
[113]	2013	A. Sherif et al. Architectural+1 Energy Performance Fixed Shading
[112]	2012	A. Sherif et al. Architectural+1 Energy Performance Fixed Shading
[130]	2014	M. Manzan Non-architectural+1 Energy Performance Fixed Shading Visual Performance

LCC Performance

Fixed Shading Non-residential

Hybrid shading Non-residential Non-residential

building

Simplified and fictitious

fictitious

Field investigation & Simulation

Theoretical & Simulation

- Lightscape -
- ESP-r Daysim -

SketchUp OpenStudio EnergyPlus -

Non-architectural+2 Non-architectural+2

Residential building Simplified and Residential building Simplified and

Theoretical & Simulation

DesignBuilder - EnergyPlus -

[129] 2009 M. Manzan and

Non-residential building

Non-architectural 2 Energy Performance Fixed Shading Non-residential

fictitious

Simplified and fictitious

Simplified and

Theoretical & Simulation

Theoretical &

modeFRONTIER ESP-r Daysim NSGA-II

Multio-bjective

modeFRONTIER ESP-r Radiance

F. Pinto +

building

fictitious

Simulation

Genetic Optimization (MOGA-II)

[122] 2005 D. R. Ossen et al. Architectural+3 Energy Performance Fixed Shading Non-residential

building

Simplified and fictitious

Theoretical & Simulation

- eQUEST - NSGA-II

Self-adaptive

building

- -

[131] 2019 A. Kirimtat et al. Architectural+1

Non-architectural 3

Energy Performance Visual Performance

Fixed Shading Non-residential

Simplified and fictitious

Theoretical & Simulation

EnergyPlus Radiance

continuous genetic algorithm

with differential evolution (JcGA-DE)

fictitious

SketchUp

H. Sghiouri et al. Non-architectural+4 Energy Performance

Thermal Performance

[128]	2018
[141]	2017
[114]	2008
[115]	2016
[116]	2013
[117]	2018
[118]	2017

Fixed Shading Residential building Simplified and

Theoretical & Simulation

jEPlus+EA TRNSYS3D

TRNSYS NSGA-II

Manzan and
1. Clarich

Non-architectural+2 Energy Performance Fixed Shading Non-residential

Simplified and fictitious

Theoretical & Simulation

- modeFRONTIER ESP-r Daysim FAST algorithm

M. C. Ho et al. Architectural+3

Non-architectural+2	Visual Performance	building fictit
Lau et al. Non-architectural+4	Energy Performance	Fixed Shading Non-residential
Hijleh building fictit
osh and S. Non-architectural+2 Energy Performance Fixed Shading Non-residential Simpli eogi building fictit

Energy Performance

Fixed Shading Non-residential

Simplified and

ious

building

Field investigation & Simulation

IES-CPC

Lightscape

- -

A. K. K.

F. Yassine and

B. A.

A. Gh

building Non-architectural+4 Energy Performance Fixed Shading Non-residential

Simplified and fictitious

Simplified and

ious

fied and ious

Theoretical & Simulation

- IES-VE -
- IES-VE -

SketchUp - EnergyPlus - EnergyPlus

building

F. F. Hernández et al. Non-architectural+5 Energy Performance

Visual Performance

Fixed Shading Non-residential

Simplified and fictitious

Theoretical & Simulation

SketchUp OpenStudio

TRNSYS

Evalglare Daysim Radiance

Table 3. Cont.

Algorithm

Ref Date Author(s) Background Design Proposals Category Building Type Design Practice Approach Modeling Tools Platform/Plug-in Simulation Tools Optimization

Energy Performance

[136] 2019 E. T. Cachat et al. Architectural+4 Visual Performance

LCC Performance

Fixed Shading Non-residential

Simplified and fictitious

Theoretical & Simulation

Grasshopper

Rhino Ladybug & Honeybee Octopus

fictitious

EnergyPlus Radiance

Genetic algorithm

2019 S. Liu et al. Architectural+3

Non-architectural 2

building

Energy Performance Fixed Shading Residential building Simplified and

Theoretical & Simulation

- EnergyPlus -

2015 M. Alshayeb et al. Architectural+3 Energy Performance Fixed Shading

[107] 2013 L. Bellia et al. Non-architectural+3 Energy Performance Fixed Shading

Non-residential building

Simplified and fictitious

Theoretical & - - EnergyPlus -

Simulation

[137] 2016 M. M. S. Ahmed et al. Non-architectural+4 Energy Performance Dynamic Shading Residential building

Dynamic Shading

Detailed and aesthetics

Field investigation Rhino Grasshopper

Meteotest Tailor-made

[125] 2017 A. Eltaweel and Y. Su Architectural+2

Energy Performance Visual Performance

Rhino

Non-residential building

Simplified and fictitious

Theoretical & Simulation

& Simulation

Grasshopper Ladybug & Honeybee

EnergyPlus Daysim Radiance

[127] 2015 J. González and

		F. Fiorito
[138]	2014	S. Adriaenssens et al.
[139]	2015	L. Giovannini et al.

Architectural+2

Energy Performance Visual Performance LCC Performance

Dynamic Shading

Non-residential building

Rhino

building

Simplified and fictitious

Theoretical & Simulation

Grasshopper Galapagos DIVA

EnergyPlus Genetic algorithm

Architectural+1 Non-architectural+6

Energy Performance Dynamic Shading Non-residential

Simplified and fictitious

Theoretical & Simulation

Ecotect - EnergyPlus -

Daysim Radiance

[140] 2015 M. Manzana and R.

Visual Performance

building

Non-architectural+4 Energy Performance Non-architectural+2 Energy Performance

Dynamic Shading Non-residential Hybrid shading Non-residential

Detailed and aesthetics

Simplified and fictitious

Theoretical & Simulation

Rhino Grasshopper DIVA IES-VE Daysim -

modeFRONTIER ESP-r Daysim FAST algorithm

Energy Performance

Visual Performance

building

Visual Performance	building	fictitious	Simulation	LightCalc
Energy Performance Dynamic Shading Non-residential Detailed and Theoretical & Rhino Grasshopper EnergyPlus -
Visual Performance	building	aesthetics	Honeybee

Hybrid shading Non-residential

Simplified and

Theoretical &

- Matlab BuildingCalc -

+ Simulation

Ladybug &

Daysim Radiance

Energy Performance

LCC Performance					building	aesthetics	& Simulation
[126] 2017 A. Eltaweel and Architectural 2 Energy Performance Dynamic Shading Non-residential Simplified and Theoretical & Rhino Grasshopper EnergyPlus -
		Y. Su		Visual Performance	Honeybee
				Energy Performance
[121]	2014	C. Kasinalis et al.	Non-architectural 4	Visual Performance	Dynamic Shading Non-residential Simplified and Theoretical & - - TRNSYS Daysim NSGA-II
				Thermal Performance
[133]	2014	R. Shan	Architectural+1	Energy Performance	Fixed Shading Non-residential Simplified and Theoretical & TRNSYS Daysim Genetic algorithm
[134]	2016	M. Mahdavinejad and	Architectural+2	Energy Performance	Fixed Shading Non-residential	Simplified and	Theoretical & Rhino Grasshopper DIVA EnergyPlus SPEA-2
Visual Performance					building	fictitious	Simulation	Octopus	Radiance Daysim
[123]	2015	A. Wagdy and	Architectural+1	Energy Performance	Fixed Shading Non-residential	Simplified and	Theoretical & Rhino Grasshopper		EnergyPlus -
F. Fathy			Non-architectural+1	Visual Performance	building	fictitious	Simulation	DIVA	Radiance Daysim

Dynamic Shading Non-residential

Detailed and

Field investigation

Rhino Matlab Grasshopper EnergyPlus

+ building

		Padovana
[124]	2011	M. V. Nielsen et al.	Non-architectural+3
[142]	2015	M. Pesenti et al.	Architectural 3
[143]	2016	Z. Nagy et al.	Architectural+8

fictitious

Simulation

Ladybug &

Daysim Radiance

+ building

fictitious

Simulation

building

fictitious

Simulation

S. Mohammadi

Retrospective Analysis

1. Design Pattern

Although each study case has fundamental differences in design parameters and project types, nevertheless, all collected samples showed a standard procedure. The mentioned energy-saving design research usually followed a similar design pattern, as shown in Figure 2. Energy-oriented design optimization is often broken down into multiple steps. With the start of the project, the design team needs to characterize the correlation between the surrounding environmental disturbance factors and the design objectives. Therefore, a large amount of detailed information needs to be entered. According to the customer’s needs, a conceptual scheme to meet various design variables is proposed. The first two tasks are imported into a design generation engine that includes an energy simulation module to predict energy loads and form a design process file. At the core of the design engine, these simulations are purely digital, but the results are visualized (text or graphics) and can create a convincing basis for designers and customers. Energy optimization is such a complex task, not to mention creating a good indoor environment. In most cases, there is no guarantee that the computer can always simulate a perfect solution. Improving the design based on the analysis report is another important link in the entire process. The single optimal solution or multiple suboptimal solutions obtained through simulation constitute the optimized final design. The compromise in multiple scenarios is still the test facing the design team. It should be pointed out that there can be two special cases as follows: If the design targets are reached, the process is terminated immediately or if the design runs counter to the expected results, it is necessary to restart the solution evaluation and repeat the above steps (very rare, but still exist).

Figure 2. Performance-oriented design pattern. Adapted from [146].

With the replacement of hand-drawn technology by computer-aided design (CAD) and advanced information technology, the AEC industry has many means of obtaining information and data. Especially with the combination of optimization algorithm technology, it has the possibility of entering a new epoch of design process. Generating modules can help designers infer and optimize designs with visual results, orchestration schemes, and workflows.

1. Current Status and Features

As the awareness of the potentially damaging effects of severe depletion of natural resources has increased, industries related to building design and management have responded by focusing on overall ecological and environmental performance while ensuring that functions and spaces are not overlooked. In this context, performance-driven design has become a new paradigm. Obviously, the statistical work on the literature is of great significance. The scope of this review covers all details of design optimization, including the subject area of research experts, project type, solution fineness, case study approach, optimization and performance objectives, energy simulation module, and optimization algorithm (Figure 3).

Figure 3. Graphical summary of all statistical works.

The first review node is the academic or technical background of all experts and scholars involved in this work, because the conclusion of this test can logically indicate the leading practitioners and research status of the topic. The data show that a total of 321 experts participated in 99 literature works reviewed. The affiliation of 100 authors indicated that they worked in the college or department of architecture, accounting for 31%. In contrast, the remaining 221 confirmed that they came from energy, environment, and other related fields, accounting for 69%. Conventional architectural design guidelines and methodologies cannot afford scientific calculation and quantification. Engineers and even computer technicians are currently leading the development of sustainable buildings. Most architects, as former industry leaders, lost initiative in the process.

The cases mentioned in the study are defined as two categories of residential buildings and nonresidential buildings according to service functions. Most of the published works (more than 80%) focus on the energy use of nonresidential buildings. Since the increase in housing units and the improvement of living standards, the energy consumption related to the residential construction sector has skyrocketed in the past decade [48,78,87]. Unfortunately, this grim fact still seems to have failed to attract attention. The third content of the review is the fineness of the design scheme, which can clearly and reasonably judge the actual application of the technology and the impact on the

architectural style in practical activities. Statistics show that only 10% of the design have developed detailed and unique architectural visual features, while simplified and virtual buildings account for 86%. Design was absent in the remaining 4% of the work. On the one hand, such a low proportion of detailed design examples show that in most building practice, the actual promotion of this new technology has a huge gap as compared with the expected goal. On the other hand, it also confirms the inherent complexity and contradiction of the discipline of architecture. Based on the description of the design stage in the literature shown in Tables 1–3, the methods applied in the above studies were analyzed in depth. The results show that in the current stage of work, the method of establishing theoretical models that meet design goals and predicting energy data through simulation tools still dominates. Only 5% of the studies showed experimental models or reduced-scale conceptual models based on design intent during the process. Building a mockup room or full-scale design prototype after computational analysis and evaluating the actual energy consumption by simulating the on-site physical environment measurement data during the operation phase should have been very critical and convincing technology. It is regrettable that only a very small part of the research has covered this part of the content.

1. Objectives and Optimization Techniques

Starting a performance-oriented design optimization loop usually means that designers seek to minimize the cost of building initialization or operation while ensuring a comfortable indoor environment. In a sense, it also includes the long-term sustainable target of building life-cycle costs. Optimization goals are often described as the number and form of combinations of design variables. In general, straightforward solutions to a single objective or exploration of multiple complex problems are common definitions. According to the objective function defined by the design conditions, a single objective solution means seeking a global maximum or minimum. Forty-three percent the of studies addressed a single design problem. Instead, it is more common to require optimization of multiple objective functions simultaneously. Two other cases applied weighted sum methods to handle the multilevel decision problem and realized the combination of conflicting targets into a single solution [65,76]. Because the core topic of this review is building energy performance, the research content of all literatures is directly related to energy optimization. The most intuitive optimization goal is the annual energy cost. Moreover, the specific forms of performance objectives are diversified. Most of the studies are interested in maintaining visual comfort or lighting quality, and the related performance goals account for 56% of all the literature. It is to be observed that improving thermal comfort or reducing discomfort time, as well as minimizing operating and maintenance costs and reducing emissions are also two performance goals that have been mentioned many times, each occupying 22% of the research material.

Design boundaries are often composed of contradictory functional requirements. The process of

solving one problem can cause the other objective functions to deteriorate. As a result, in order to locate these variables and perform targeted quantization and tradeoff of functions, algorithms are needed [31]. A set of solutions is generated by a multiobjective algorithm and plotted in the form of a curve called a Pareto frontier. The optimization algorithms shown in Figure 3 can be defined as three types [147]. The analysis data show that among all optimization problems, the evolutionary algorithm represented by genetic algorithm (GA) is used most frequently. The search algorithm represented by Hooke–Jeeves and the hybrid algorithm represented by PSO-HJ have also been found in research papers, but the frequency and number are far lower than the former category. It should be clear that not all design processes require the participation of optimization algorithms.

Energy simulation tools are vital modules in optimizing design workflows. They are widely used in energy calculation and decision analysis. EnergyPlus accounted for 57 of all 99 research papers, far ahead of other simulation engines. Similar tools used in other work include several types including TRNSYS. Custom-tailor based tools were also found in multiple records. The research project determines the completeness and complexity of these tools. It can be a complete application

that is used to evaluate complex designs or it can be just a plugin to analyze a simplified benchmark model. Furthermore, most literature does not specify the tools used to implement three-dimensional (3D) models.

The most frequently used optimization algorithms and simulation tools mentioned earlier are not accidental. Some typical characteristics make it stand out in the field of practical application. Firstly, this tool has been identified as architect friendly for architects and engineers involved in decision making. Secondly, the technology transmits data from multiple work platforms and can quickly analyze and feedback input and output parameters. Finally, the software supports the comparison of multiple alternative models [30]. Taking the application of genetic algorithm and EnergyPlus in design optimization as an example, there are already many engineers familiar with easy-to-use work platforms coupled with energy simulation engines [34]. Matlab [148] is one of the most trusted products. Matlab developed by MathWorks combines multiple simulation tools for different research areas, and EnergyPlus for building energy simulation is one of them. The developed toolbox provides multiobjective optimization for linear or nonlinear problems. In addition, a custom programming toolkit for complex environments provides practitioners with ease-of-use. Encouraged by the development of well-developed commercial software, architects have also been assisted in promoting sustainable movements. At this point, Rhino [149] is a fairly mature open design software that integrates grasshopper [150] platform and also realizes parametric design through simple code. The Galapagos [151] module embedded in grasshopper uses genetic algorithm to optimize the objective function. Ladybug and Honeybee [152], a designer friendly plug-in, provides the service of energy consumption simulation in a rhino and grasshopper environment and allows users to work with the verified energy plus to get feedback.

Industry Dividends and Potential Challenges

1. Heuristic Knowledge Base

Currently, the AEC industry relies primarily on life-cycle approaches to make buildings more sustainable. It systematically quantifies the potential impact on the environment of a building from conceptual design to termination of service [153]. Traditional design methods tend to integrate quantifiable standards only in late stages. Contrary to earlier designs, evaluating the execution efficiency and applicability of a design mainly depends on the designer’s self-knowledge of architecture and focuses more on a limited range of applications such as functionality and aesthetics. The evaluation of other properties (such as energy consumption, thermal comfort, and other related aspects) is usually delayed. Such methods have conspicuous limitations [154]. To mitigate the negative impact of buildings on the environment, it is particularly important to evaluate the energy consumption in the early stages of design [155]. As shown in Figure 4, the earlier the assessment, the more obvious the benefit. Full consideration is given to the possibility that the success of the solution is largely influenced by the choices made at the initial stage, so the involvement of a more professional comprehensive judgment process is necessary [156]. The paradox is that even at the conceptual scheme stage there are still too many parameters and information to be weighed. Although sensitivity analysis is available, it is considered to be too complicated, not to mention that paying too much attention to detailed variables can lead to a lack of creative work. In this case, applying an evaluation model based on a heuristic knowledge map can provide the necessary help.

As described in the previous section, only a small number of studies have developed a detailed scheme design and conducted experimental or field investigations, but it does not mean that the value and contribution of those theoretical studies can be easily denied. In a recent analysis, building orientation was considered one of the key optimization variables in the early design phase [39,57,61,64,67]. Optimal orientation can effectively reduce the negative effects of solar radiation, thereby correspondingly reducing the use of heating or cooling energy. The window-to-wall ratio is another important parameter that affects the initial design. A suitable size can compensate for

insufficient ventilation and lighting. On the contrary, it causes unnecessary energy consumption. A lot of correlation knowledge emerged in this survey to make up for the data gap at this stage [40,42,48,51,55,62,65]. Changes in the data of reference models for different climate information are usually not recorded or tracked. The schematic diagram and its parameters determined by evidence-based methods are provided as default values to users and other tasks that require rapid simulation [49–51,59,66,73]. Obviously, all professional information acquired based on experimental experience, engineering judgment, a series of trials and errors, and catalog information also belongs to the core component of the expert knowledge system [41,50,75,109,114,137,143]. They are as effective as sensitivity and uncertainty analyses performed by users through manual intervention. A heuristic knowledge design approach that incorporates energy awareness is one of the direct benefits created by the above research. It is characterized by the establishment of a responsive knowledge base between design decisions and data lists. This is a highly interdependent process between contexts. Alternative tools combining rules of thumb and extensive knowledge accumulation allow designers to compress the search space for almost all the variables (shape, orientation, shading, site location, etc.) necessary for the design preprocessing stage [157]. This has greatly accelerated the process of building energy performance assessment.

Despite these advantages, the design guidance methods used by knowledge-inspired systems are based on generalized information from standard case studies. When designers need to apply such rules in complex nonstandard design environments, they cannot be applicable or even run counter to the idea. Therefore, extensive exchanges and consultations with engineering and technical personnel are necessary.

Figure 4. Time-utility distribution between sustainable and conventional processes. Adapted from [155].

1. Expanding the Design Space

The object-orientated design process integrating parametric information model technology, energy consumption simulation, and evolutionary algorithms is a rapidly emerging and popular method of sustainable building design. The purpose is to shift the workload to the early stages of design and to add a series of iterative activities in the scheme generation [158]. The conventional design process based on trial and error requires direct search of components with different configurations and parameters, and its ability to solve problems depends on the commonality between the problem and the existing design solution samples. It is not only time-consuming but also severely imprisons the innovation of design. In the design method of the correlation target variable and the optimization technology, the user can explicitly formalize the design concept and performance target by constructing a digital model in advance. The model is interpreted as a typical set of unrestricted instance variables according to the simulation experimental data, and each instance is determined by the model-specific independent variable selection, thereby expanding the expressiveness and scope of the constructed scheme [159].

This is why in the multiobjective design, all the options in the solution space are described as an approximate optimal solution in the design process in combination with the target requirements. In fact, one of the important effects of constraint variable agents is that transformations are performed, resulting in multiple combinations of the same component which automatically supports the generation of larger design variant sets within a controlled degree of freedom, providing a wider variety of alternative models and schemes [160]. Occupants’ behavior has long been considered to be one of the uncertainties in improving building performance. The novel generative framework describes and models typical occupant behavior in buildings in various forms [161]. The simulation tool for coupled optimization algorithms allows users to write code to customize configuration files and control rules for device components and operating systems. A series of recent studies have shown that this technology can be applied in different scenarios including: reducing the air conditioning load of homes in humid climates [162], improving the efficiency of domestic hot water systems in hotel buildings [163], and properly controlling lighting equipment [164]. The industry dividend brought by a more flexible generative design method is to break the boundaries of different disciplines and overcome the limitations existing in the conventional design process. The expanded design space means enhanced access to performance-oriented tasks and strategies.

For designers, this promising practical technology has proven to be indirectly beneficial in highlighting the visual aspects of architecture and discussing new aesthetic standards. The design solution generated based on the innovative method not only quickly views different alternatives, but also realizes a geometric configuration where a large number of possible combinations of variables in the mapping domain cannot be conceived (Figure 5). In the novel process, rules are replaced with rule types, and a set of composable and navigable operators is used to generate a complex function space and geometry based on a unified parser [165], assisting decision makers by revealing new design directions while focusing on engineering performance standards, and also helping explore areas of design that have never been involved before. Whether a positive relationship exists between energy-driven design and architectural aesthetics is the starting point of this review. Over the past period, large-scale photovoltaic panels have ruggedly extended from the building roof to the site, and wrong demonstrations have appeared on both sides of the street. The correct design paradigm can eliminate public biases and misunderstandings about the formal characteristics of sustainable architecture.

Figure 5. Studies on different components. (a) Building envelope [45]; (b) building geometry [95];

Similar to the basic hierarchy of natural energy system classification, architecture is not an isolated object. The expanded design space is also suitable for diverse urban scenarios. A natural and harmonious urban environment is an important means to maintain ecological stability and building

performance. A sustainable building should be understood as a trinity building, including the lowest energy consumption, the best urban environment, and excellent spatial quality. Architectural aesthetics and visual creativity are also the most important components. The loss of built environment quality attributes has no place in the true sustainable development of society [166].

1. Tools, Skills, and Framework

According to AIA Energy Modeling Guidelines, architects are the most suitable members to lead the process because they have experiences in integrating functions, spaces, and global systems [167]. From the literature, the dominance of architects seems to be challenged. Some have argued that architects do not have the ability to participate in the development or promotion of energy efficiency technology, but only the ultimate beneficiaries [22]. However, from the current research status, the engineer-led performance optimization design also has missing parts (discussed in Section 3). Objectively speaking, the existing conflicts and obstacles have complex and deep causes, and it is unfair to accuse any of them unilaterally. This is a challenge that industry stakeholders must deal with in the process of sustainable development that promotes integrated innovation.

The range of various building performance simulation (BPS) tools on the market can be divided into graphical user interface (GUI) tools and text user interface (TUI) tools according to user preferences (architects and engineers), underlying architecture, and operating logic [30]. The unfriendly architecture of BPS tools was considered to be an obstacle in previous work to enable performance-based design decisions, accelerate integration of energy performance assessment, and design processes. Currently, this missing piece has been made up for by the efforts of a large number of researchers. On the basis of a mature computing engine, a graphical interface simulation tool list has been developed, which extend the existing functions, and also meets the required speed and accuracy. However, practical applications show that discontinuities in the data file exchange format for different types of BPS tools and coupling barriers to optimize the interface between software packages are current urgent tasks [21,32]. The recommended effective solution is called middleware tool [33]. This associative design platform that provides multiple interactive forms has the powerful functions of directly operating geometric models, defining simulation conditions, and writing script algorithms [168]. The component’s built-in hybrid programming language (HPL) module enables simultaneous transfer between visual and text programming interfaces. Modifications and extensions can be made by any architect, engineer, or third-party worker. Therefore, middleware cannot be simply defined as a converter of various types of data formats and input/output files between software platforms, but a system that can be deeply customized between the model level and the computing environment [169]. Unfortunately, it appears that the far-reaching impact of this technology has yet to spark widespread discussion.

Entering a new age, design has fundamentally changed in form and content. The skills gaps of

architects and engineers have been highlighted. In the current context, not only do architects have to face fierce competition from their peers, it is crucial that the learning costs of most software used to optimize building performance remain expensive, let alone complicated knowledge of mathematics, environmental science, and computer science. In the same way, it is still a huge challenge for engineers. Most engineers admit that their professional background also does not include learning experience of architectural space and aesthetic criteria. Some scholars have suggested that the main pedagogical details of architectural education are questionable, and it has failed to develop students’ ability to solve problems they could encounter in their careers [170]. In addition to a large amount of professional knowledge, decision makers are also required to keep an eye on the changing design paradigm to meet the public’s consumer demand for architectural culture [171]. It needs to be acknowledged that these demands are made up of the numerous internal and external factors in sustainable design. Creative intuition, extensive interdisciplinary knowledge, organization, and coordination skills tend to determine that one of the two professional communities will become the supervisor of the integration project.

With the emerging concept of green building and its potential, the business collaboration framework has changed. Design team members are facing new challenges to improve and refine workflow efficiency. Designers are required to describe and adjust the physical performance data of a conceptual solution from the perspective of energy conservation needs. If there are too many unidentified and ambiguous design variables, once the design iteration is performed, the associated man-hour cost of modification increases significantly. The innovative position of engineers as operators of computing models also needs to be guaranteed. When engineers are in the process of calibrating the calculation model, the risks associated with transmitting modification information that deviates from the design concept are more profound. Compared with the hierarchical and structural features of traditional workflow which are completely separated and unable to communicate and feedback, design team needs a novel hybrid driven framework. Architects and engineers no longer have clear business boundaries. As common stakeholders, they should have equal professional awareness in technical analysis and architectural design. The hybrid model needs to define more cross-disciplinary horizontal communication mechanisms, so that complex design problems can be solved collaboratively and efficiently.

1. Calibration Model

In general, the distinction between the calculated energy consumption in the building performance test phase and the field measurement data in the application phase is called the “performance gap” [24]. A great deal of research has become interested in this phenomenon. These studies explored this key issue from the perspectives of building energy and environmental monitoring [172,173], occupants’ schedules and behavioral characteristics [168,174], building energy model verification and test benchmarks [20,26], and so on. Too many clues remind us that, for various reasons, deviations in building performance are theoretically inevitable. These studies provide valuable systematic knowledge to architects, engineers, and other decision makers. However, among the various bridging solutions presented, the establishment of a calibration model at the design stage is still missing.

The establishment of a calibration model should include two indispensable elements. Making prototype models of different metrics according to design decisions is the primary content of this work. As the project progresses, it eventually develops into a full-scale mockup (different from the digital model used for simulation) that can accurately restore all details. Subsequently, with the help of intelligent wireless monitoring equipment, a full range of on-site demonstrations are conducted. Unlike existing prediction models that only consider the annual energy consumption of a building, the biggest advantage of the calibration model is that it can monitor seasonal, monthly, daily, or even hourly changes in realistic scenarios. The recorded data can provide practical and effective solutions for improving the energy-saving performance of similar types of projects, especially in mixed climate backgrounds.

The working methods and collaboration mechanisms demonstrated by the Foster & Partners team during the design of their client’s European headquarters building project are instructive. The architect creatively designed a “breathing façade” combined with a natural ventilation system. In order to avoid an imbalance between the actual construction effect and the computer simulation result, the design team went through multiple experimental phases. First, a miniature model test of 1:100 was performed. The glass model was immersed in water and dye was injected to visualize the entire ventilation process. It was subsequently expanded to full-size mockups and real-world scenarios, allowing the introduction of air at the required temperature for relevant tests (Figure 6). The model was extensively tested and calibrated on each work area [166]. The building was rated BREEAM Outstanding, which can be largely attributed to the tireless efforts of research and testing.

Figure 6. Prototype testing [175].

Conclusions and Future Work

1. Conclusions

In architectural discourse, the same vocabulary used to describe performance has been shared for a long time, but the content it represents varies dynamically in different contexts. This review takes the correlation hypothesis between performance-oriented design optimization and architectural aesthetics as the starting point and focuses on the opportunities and challenges faced by architects and other professionals in the process of design paradigm changes. From a large number of published papers and reports, 99 were recommended as core materials. Through combing the work accumulated in this research field for nearly three decades, the in-depth analysis and discussion of general processes, optimization and performance objectives, simulation modules, and optimization algorithms were conducted.

Performance-oriented architectural design is undoubtedly a complex technical system involving multiple disciplines. But the generative design patterns established by the researchers presented in all cases are consistent. It contains a number of closely connected components such as building information model platform, energy simulation engine, and optional optimization algorithm module (Figure 2). In addition to the design objectives and parameters specified by the decision makers during the solution generation process, the rest is highly integrated and dynamic.

With rapid changes in design patterns, the traditional role assignments of architects, engineers, and other professionals in decision-making teams have shifted significantly. Only 31% of the participants in the study identified themselves as architects, which triggered a series of chain reactions. The vast majority of projects and studies (over 85%) only provided theoretical calculation models or simplified design solutions that abandon architectural style and visual characteristics. Only 5% of the work showed experimental conceptual models or micro-scale prototype models made according to design intent.

To date, more attention has been given to multiobjective optimization. In addition to building energy performance (annual energy consumption), research has generated great interest in visual performance (indoor lighting environment) or thermal performance (comfort index) and life cycle costs. In order to achieve multiobjective optimization, evolutionary algorithms, search algorithms, and hybrid algorithms are widely used. In these directories, the family of evolutionary algorithms represented by genetic algorithms occupy an overwhelming advantage. Correspondingly, EnergyPlus has become

the most commonly used energy calculation engine in performance-oriented building design. Other tailored simulation tools have also been discovered. Current trends reveal that selected tools must undergo a dual assessment of timeliness and robustness. The proposed survey shows that as compared with conventional design strategies integrated optimization has stimulated extensive discussions on architectural design paradigms. All performance-based analysis data, simulation models, and evaluation results are assembled into an expert knowledge base that supports energy-conscious design and optimization. The heuristic method expresses information characteristics related to energy consumption in the form of design variables (parameters) and their relationships. Energy-saving rules are applied in benchmarking models. Decision makers enable expert knowledge and empirical judgment based on context sensitivity to assign values to the default information, thereby effectively improving the overall execution of the design.

The second benefit is directly related to the holistic consideration of form and energy. Integrated optimization undoubtedly promotes the breadth and depth of design scope. The building information model coupled with energy consumption simulation and multiobjective optimization algorithm can quickly realize visualization of different scenarios. Alternate combinations based on multiple variables have resulted in many unpredicted geometric configurations in the generated design. Both factors help expand the space for viable design solutions, as well as reveal architectural styles and features that were not previously expressed.

Recent developments in sustainable movements have inspired innovations and challenges in the applications and technologies that support the AEC industry. In most scenarios, geometric models are expected to exchange data with computational and algorithmic models in both directions, but this situation rarely occurs in dynamic coupling programs. The extreme lack of middleware for unifying software environment interfaces and exchanging data formats is the primary obstacle faced. As the design paradigm shifted, architects and engineers discovered what was missing in their reserves of expertise and vocational skills. These factors greatly limit the applicability of the technology in real-world practice. Moreover, the huge differences in target motivation, operating regulations, and evaluation criteria between the two groups are also issues that cannot be ignored in the working framework.

In order to solve the aforementioned performance gap, the establishment of extensive calibration models is also an indispensable part of the design process. Developing a full-scale mockup that reflects design intent and details is considered to be an extremely important step. Accurate data based on comprehensive on-site monitoring was eventually used to evaluate the objective results of the design scheme under changing climate conditions.

1. Future Work

Despite considerable research on building energy performance optimization, there are still unsolved issues before it can be widely used in the AEC sector to form incentives and quantification criteria. Therefore, the following future work is recommended:

One of the most pressing issues is to raise awareness of planners and architects to consider the effectiveness and necessity of energy consumption and its expected impact on sustainable development. An insightful design tool based on pattern language is thought to have a positive impact on the design process. The scientific discourse on language and its systematic use in the design process has a strong tradition in planning and architecture. The language tool describes the responsive knowledge of the mapped energy as a matrix diagram nested within each other. Abstract architectural physics phenomena are transformed into concrete design strategies. The essential goal of energy model language is not to negate and abandon existing technologies. Instead, it is similar to an extension package loaded in a tool kit, which is responsive to the environment.
From the perspective of pedagogy, performance-based architectural design is a method that relies on professional skills and tacit knowledge to solve comprehensive design problems that combine

the physical environment and material quality. On the basis of this prerequisite, vocational education in architecture should start to improve from both the basic curriculum and design training. In the process of shaping the students’ theoretical system, interdisciplinary knowledge should be gradually connected at different stages. In design training, on the one hand, students need to be guided and encouraged to use computer programs to carry out a holistic analysis of the solution. Thereby obtaining fairly reliable data and parameters. On the other hand, it is necessary to advocate and highlight the creative part of the design, and therefore prevent candidates from falling into the persistent misunderstanding of technology-only thinking.

Although a general design generation model has been developed, its accuracy in predicting building energy consumption simulation still needs to be rigorously evaluated. To bridge the gap in building performance, a promising approach is to build a hybrid model that fully reflects the behavior of the occupants. In this project, simulation engines and optimization algorithms were not selected as the focus of work. Data acquisition methods combining physical environment and socioeconomic factors are research directions. A holistic approach and roadmap should be used to determine the appropriate sample size, instrument deployment, and monitoring period, which is a top priority.

Author Contributions: Conceptualization, S.L. and C.P.; methodology, C.P.; validation, S.L. and L.L.; formal analysis, S.L.; investigation, L.L.; data curation, S.L.; writing—original draft preparation, S.L.; writing—review and editing, S.L. and C.P.; visualization, S.L.; supervision, C.P.; project administration, C.P. All authors have read and agreed to the published version of the manuscript.

Funding: This study was supported by the Ministry of Science and Technology of the Peoples Republic of China (Key Projects of Technological Innovation for Green Livable Village, grant no. 2019YFD1100805), and the Fundamental Research Funds for the Central Universities and Postgraduate Research & Practice Innovation Program of Jiangsu Province (grant no. KYCX17_0109).

Acknowledgments: Any opinions, findings, conclusions, or recommendations expressed in this study are those of the authors and do not necessarily reflect the views of the committee.

Conflicts of Interest: The authors declare no conflict of interest.

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Journal of Cleaner Production 387 (2023) 135921

Contents lists available at ScienceDirect

Journal of Cleaner Production

journal homepage: www.elsevier.com/locate/jclepro

Review

Design variables affecting the environmental impacts of buildings: A

critical review

Yijun Zhou, Mingxue Ma, Vivian WY. Tam *, Khoa N. Le

Western Sydney University, School of Engineering, Design and Built Environment, Penrith, NSW, 2751, Australia

A R T I C L E I N F O

Handling Editor: Genovaite Liobikiene

Keywords:

Design variables Environmental impacts Building design

A B S T R A C T

Building sector is responsible for a large portion of global environmental impacts. The life-cycle environmental impacts of a building are largely determined by decisions made during design process. For making eco-friendly building design decisions, a prerequisite is to understand which design variables affect the environmental im- pacts of a building over its life cycle is. This research conducts a critical review to identify design variables affecting the environmental impacts of buildings at three design stages during the design processes. Publications between 2010 and 2022 (inclusive) are examined and 50 papers are selected for review. The results reveal that eight design variables in early design stages, including (1) building aspect ratio, (2) window-to-wall ratio, (3) shading area, (4) building orientation, (5) number of floors, (6) building shape, (7) floor area and (8) floor-to-

floor height, have an impact on a building’s life-cycle environmental impacts. In detailed design stages, there

are four kinds of design variables linked with the environmental impacts of a building: (1) types of building components, (2) sizes of building components, (3) types of building materials, and (4) thickness of building materials. Types of finishing materials are closely related to the environmental impacts of a building in con- struction design stages. The findings of this research help understand the contributors to the environmental impacts of a building from the perspective of building design. The findings also provide a future research di- rection on design variables for making eco-friendly building design.

Introduction

Environmental impacts of a building refer to changes to the envi- ronment resulting from activities, products, or services in the con- struction work or in the use of the construction work (ISO 14001, 2015). Examples of activities that can cause environmental impacts by a building are the use of energy, emissions to air, water use, and land use (European Committee for Standardization, 2011). Environmental im- pacts include climate change, air pollution, water pollution, and land pollution, and they are expressed by environmental impact categories such as global warming potential, and primary energy demand (Euro- pean Committee for Standardization, 2011). Rapid changes to the climate over the past years are mainly attributed to energy consumption and greenhouse gas emissions (such as carbon dioxide and methane) related to human activities (Stocker et al., 2013). Therefore, to avoid global climate getting more serious, great efforts need to be put into addressing carbon emissions and improving energy efficiency.

Carbon emissions of a building occur over its life cycle from the acquisition of raw materials, construction, and operation of the building

to its final disposal at the end of the building life cycle (European Committee for Standardization, 2011). Carbon emissions related to building operations are called operational carbon, which refers to the carbon emissions released from all energy used to keep the building warm, cool, ventilated, lighted, and powered. In 2020, carbon dioxide emissions from building operations accounted for 37% of global energy-related carbon emissions (IEA, 2021). Moreover, the manufacturing of building construction materials produced about 3.2 gigatons of carbon dioxide (IEA, 2021). These emissions are causing an increase in global warming. Therefore, great attention should be paid to the building sector to diminish building-related environmental impacts (carbon emissions and energy use) along their life cycle.

The life-cycle environmental impacts of a building largely depend on the decisions made at its design stages (Basbagill et al., 2013). For instance, building orientation and shading areas have a great influence on the operational energy demands of buildings (Tushar et al., 2021). Different wall types used for a building lead to different amounts of annual heating annual energy demands and greenhouse gas emissions (Maoduˇs et al., 2016). An investigation by Rebitzer (2002) showed that

* Corresponding author.

E-mail address: vivianwytam@gmail.com (V.WY. Tam).

https://doi.org/10.1016/j.jclepro.2023.135921

Received 5 November 2022; Received in revised form 20 December 2022; Accepted 4 January 2023

Available online 5 January 2023

nearly 70% of the environmental impacts of a building over its life cycle are determined by building design. . Therefore, making low-carbon and energy-efficiency design decisions has great potential to reduce the adverse environmental impacts in building sector.

A building design involves determining a large number of design variables such as the choice of shape, orientation, and height of a building and the determination of material and dimensioning specifi-

cations for hundreds of building components. The decisions on design variables in practice heavily depend on the designers’ experience (Silva and Ghisi, 2020). As a result, energy and carbon-intensive design de- cisions are often made as the previous experience cannot precisely foresee the impacts of each design variable on carbon emission and

energy consumption. Given this fact, it is considered important to clarify the relationships between design variables and carbon emissions and energy consumption of a building over its life cycle.

Researchers have acknowledged the important roles of building design variables in creating environmentally preferred design solutions. Numerous studies were conducted to explore the relationships between design variables and the environmental impacts of a building. One mainstream of the studies focused on which design variables are the most influential in terms of environmental impacts and which design variables are less important. For example, Silva and Ghisi (2020) examined the contribution of 21 design variables on the energy con- sumption of a building, and the results highlighted the thermal trans- mittance and the solar absorptance of the roof and the window area. Another study investigated the importance of different types and thicknesses of building materials in embodied carbon emission (carbon related to the production of building materials and construction of a building).Cladding materials were found to have great potential to lower the embodied carbon, while the changes to the types and thicknesses of services components played less important roles in reducing carbon emissions(Basbagill et al., 2013). The design variables examined in these studies are limited and are subjectively selected by researchers, despite the fact that tthey are significant. However, some variables with great influence on building environmental impacts are probably not included for analysis of their importance. This may, in turn result in incom- prehensive and constrained conclusions on the determination of eco-friendly design solutions. Another mainstream of the studies attempted to review the design variables affecting the energy con- sumption in building operation. For example, thermal mass, thicknesses of insulation factors, window sizing, glazing, and shading were identi- fied to have great impacts on the energy efficiency of a building in a wealth of literature (Albatayneh, 2021; Islam et al., 2015; Wang et al., 2021). Despite being useful for understanding the relationships between design variables and the use of energy at the operational stages of buildings, the influence of design variables at other stages (such as building construction and demolition stages), which are as important as the operational stages, has been neglected.

Above discussion reveals that previous attempts in the literature have investigated the relative importance of limited design variables and explored the relationships between design variables and operational energy demands. However, they fail to clarify which and how design variables affect the environmental impacts (carbon emissions and en- ergy use) of a building over its life cycle. In view of this background, the novelty of this research is to have a comprehensive and depth under- standing of design variables associated with the environmental impacts (carbon emissions and energy use) of a building over its life cycle. This research aims to conduct a critical literature review on design variables affecting the life-cycle environmental impacts of a building. The outcome of this research can have comprehensive insights into the in-

fluence of design variables on a building’s environmental performance

(performance related to environmental impacts and activities causing environmental impacts). Furthermore, the research findings can enlighten the optimization of design variables to achieve environmen- tally preferred design solutions.

The article is structured as follows. The method to examine studies

related to design variables is presented in Section 2. In Section 3, the findings on the design variables that affect carbon emission and energy use are presented. By drawing on the review findings, recommendations for future work on building design variables are proposed in Section 4. The conclusions are presented in Section 5.

Method

To fulfill the research aim, this research adopted a critical literature review method for examining studies related to design variables affecting the environmental impacts (carbon emissions and energy use) of buildings throughout their life cycle. Critical literature review is a powerful method to advance the knowledge of specific topics and to comprehend the values of researchers in academia. For this study, the application of the critical literature review method can provide a comprehensive understanding of design variables associated with building environmental performance. Moreover, the effectiveness of applying this method in building and construction fields has been evi- denced in previous studies (Tam et al., 2022). The critical review process adopted in this study was adapted from the review process developed by Osei-Kyei et al. (2021). Two aspects of the review process were improved in this study: first, this study retrieved publications from more scholarly publication search engines to assure relevant sources are not left out. Moreover, this study established a two-round paper identifica- tion procedure with strict rules to filter publications. Fig. 1 illustrates the research framework of this study.

Critical literature review starts with determining the search engines. The scholarly publication search engines, Scopus and Web of Science were chosen for literature searching in this study because they cover critically influence abstract and citation databases in building and construction fields.

Then, a full search of papers about design variables and building

environmental impacts was conducted in these databases from 2010 to 2022. By searching the following keywords: (TITLE-ABS-KEY (“life cycle assessment” OR “life cycle analysis” OR “environmental impacts” OR “environmental performance”) AND TITLE-ABS-KEY (“design variable” OR “design parameter” OR “design factor”) AND TITLE-ABS-KEY (“building”) AND TITLE-ABS-KEY (“hous*” OR “residential” OR “Dwelling”)) in academic databases of Scopus and Web of Science, there

are 223 publications in total. The literature retrieval results are limited to peer-reviewed journal articles written in English.

In the third step, the preliminary results were filtered by adopting a two-round article selection to ensure the filtering quality. In this first round, the title, abstract, and keywords of these publications were scanned and checked, 138 publications were discarded because they only mentioned the concept of design variables but failed to show spe- cific variables. Then the whole texts of the remained 85 publications were critically reviewed in the second-round selection. The filtering rule in the second round was to discard design variables not related to the environmental performance of buildings. After the two-round filtration, 42 publications were retained for analysis. Subsequently, a snowballing

cross-reference search was done by scanning the publications’ titles in the reference section and their context and cited content in the text. The

abstracts of the identified additional publications were scanned to determine whether the paper was relevant. Relevant references were added to the sample and analogously scanned for relevant cross- references. This process was repeated until no further relevant cross- references could be identified. After the relevance checks, contributed

8 additional publications. Ultimately, 50 papers were retained and included for analysis. (See supplementary data- Appendix A).

Finally, the contents of the 42 selected papers were comprehensively analysed to identify the design variables affecting the life-cycle envi- ronmental impacts of buildings during the design process. The envi-

ronmental impacts of a building over its life cycle can be assigned to four modules in line with the stages where the building’s environmental performance occurs (European Committee for Standardization, 2011).

Fig. 1. Research framework of this study.

The four modules are Module A1 to A3 (also known as “product stage”), Module A4 and A5 (also known as “construction process stage”), Mod- ules B1-B7 (also known as “use stage”), and Modules C1-C4 (also known as “end-of-life stage”). The product stage covers the impacts produced by ‘cradle to gate’ processes for the materials and services used in the

construction. All impacts due to the processes from the factory gate of different construction products to the practical completion of the building are assigned to the construction process stage. The use stage includes all the impacts related to the use of the building and services for protecting, moderating, or controlling the building, such as heating, cooling, lighting, and water supply, and scenarios for maintenance, repair, and replacement of the building. In the end-of-life stage, the impacts produced in the process of deconstruction, transport, and waste processing for reuse, recovery, or recycling are included. Moreover, the environmental impacts generated in the product, construction process, and end-of-life stages are defined as embodied environmental impacts, while impacts produced by energy and water consumption in the use stage are defined as operational environmental impacts. Moreover, the net environmental benefits resulting from reuse, recycling, and energy recovery are quantified in Module D, which is beyond the system

boundary of the building life cycle and is not the focus of this research. On the other hand, the general design process of buildings could be divided into three stages in line with the content and depth of the building design, namely early design stages, detailed design stages and construction design stages (Cang et al., 2020). Each design stage has different tasks, requirements and design variables. Design variables in early design stages are related to the geometrical configurations of a building, such as the shape factor, and window-to-wall ratio, while design variables in detailed design stages concern the types and speci- fications of components and materials (MOHURD, 2016). The con- struction design provides exact information on each material to finalize the building design solution (Cavalliere et al., 2019). Design variables in construction design stages, therefore, focus on the finishing of building components. The influence of design variables in three design stages on the environmental impact in the four modules was captured and dis-

cussed in the following section.

Results and discussion

The results are presented and discussed from two aspects: 1) design

variables that have an influence on the environmental impacts of buildings in three design stages, and 2) how each design variable affects the building environmental impacts.

1. Design variables in three design stages

The design variables affecting the environmental impacts of build- ings over the life cycle in each design stage are captured from reviewed articles and illustrated in Fig. 2. Fig. 2 also shows the number of pub-

lications that include the design variables affecting the building’s

environmental impacts.

The results revealed that there are eight design variables affecting the environmental impacts of a building at the early design stages. They are 1) building aspect ratio, 2) building orientation, 3) wall-to-wall ratio, 4) number of floors, 5) building shape, 6) façade type, 7) floor area, and 8) floor-to-floor height. The eight variables contribute to the life-cycle environmental impacts of a building. Moreover, according to

Fig. 2, “building orientation” and “window-to-wall ratios” are the most popular design variables in the publications collected, which appeared

in 27 and 24 publications respectively. They are followed by “shading shape” which has been investigated in 16 publications. However, limited research has been conducted on façade type in terms of building

environmental impacts.

Thirty-four design variables in detailed design stages were identified to have an influence on the environmental impacts of a building, as shown in Fig. 2. The type of roof membrane, type of wall membrane, and insulation type for floors were examined in only one publication and were found to be associated with embodied impacts. 16 design variables

were identified to affect the energy demands in building operations. In this regard, the “Heat transfer coefficient of windows”, which is dependent on the selected type of window, has been examined the most (in 13 publications). Nevertheless, the specifications and thickness of

walls were less explored in studied related to building energy demand., Moreover, 15 design variables were linked to the life-cycle environ- mental impacts of a building. Types and thicknesses of insulation ma- terials for walls received great attention from researchers and were closely related to the life-cycle environmental impacts of a building. However, less attention has been paid to the environmental impact of insulation types for ceilings, and construction materials for roofs, ceil- ings, and floors. The design variables in detailed design stages could be classified into four kinds in line with the features of building compo- nents and materials: 1) types of building components, 2) specifications of building components, 3) types of building materials, and 4) thick- nesses of building materials.

According to the results shown in Fig. 2, in the construction design stages, the types of finishing materials for walls and floors were iden- tified to affect the embodied environmental impacts of a building. Nevertheless, there were a limited number (only one in the publications

collected) of studies on the type of finishing materials (Simko and Moore, 2021).

1. Influence of design variables in early design stages

The environmental impacts (carbon emissions and energy use) of each design variable in the early design stages over a building’s life cycle are presented as follows.

- 1. Building aspect ratio

Building aspect ratio refers to the ratio between the building’s length and width, which is a significant determinant to solar heat gains and energy efficiency (Hachem et al., 2011). Different building aspect ratios

lead to different heat conduction and solar radiation received by building envelopes, thereby affecting the heating and cooling loads of buildings (Zhang et al., 2020). In referring to the effects of building aspect ratios on the embodied environmental impacts, the length and width of buildings were regarded to have a close relationship with embodied carbon emissions of buildings (Yu et al., 2022). However, previous research failed to examine how carbon emissions depend on building aspect ratios. Therefore, the environmental impacts of building aspect ratios need to be quantified, and an efficient aspect ratio should be designed to maximize building performance.

In previous studies, the effects of different building aspect ratios on operational energy efficiency were examined together with other pa- rameters, including orientation, building shape, glazing-to-façade ra- tios, etc. For instance, an increase in heat loss was observed for north-, west- and east-oriented building zones with the aspect ratio of 1: 1 (Kontoleon and Zenginis, 2017). Thermal performance of south-oriented building zones with openings improves along with an increase in aspect ratio (Kontoleon and Zenginis, 2017). In addition, the optimal aspect ratio for heating and cooling savings attracted academic attention. A rectangular shape solar house with an aspect ratio between 1.3 and 1.5 has an ideal solar insolation level (Hachem et al., 2011). However, the optimal aspect ratio for heating efficiency could be different from the ratio for cooling efficiency, which means that it may be unable to ach- ieve them simultaneously (McKeen and Fung, 2014).

- 1. Building orientation

Building orientation refers to the direction that the long façade of a building faces. Building orientation has very limited impacts on embodied energy (Monteiro et al., 2021), while it is critical to energy

consumption during the operation phase. Building orientation is in relation to the sun’s path. Setting the long side of a building to face different orientations could receive different solar contributions and have different energy demands for heating and lighting (Wong and Fan,

2013). Heating and lighting systems are two significant contributors to the energy consumption of buildings (Abanda and Byers, 2016).

Fig. 2. Number of publications that include design variables affecting environmental impacts at each design stage.

Therefore, it is critical to properly orient a building to improve building energy efficiency.

A great number of previous studies used a series of orientations to calculate energy consumption. For instance, Fallahtafti and Mahdavi- nejad (2015) studied the heating loads of 48 orientation angles (between 270 and 318◦ from south-west to south)for the optimized orientation. The optimized orientation for the building was between 308 and 318◦

(Fallahtafti and Mahdavinejad, 2015). Lobaccaro et al. (2018) varied building orientations and found that northwest–southeast was the best-optimized orientation. A total of 194,000 kWh/a solar radiation could be collected with optimized orientation, while northwest and northeast-oriented façades could only receive 33,000 kWh/a (Lobaccaro

et al., 2018). Therefore, facing south is generally the ideal orientation for buildings in the north hemisphere for all climate conditions because the south-facing façade receives maximum solar energy (Carbonari et al., 2002). The situation differs in the southern hemisphere. In Australia, the north was found to be an ideal house orientation in most areas except Brisbane and Darwin, while fewer window areas to the north are the best orientations in sunny locations.

- 1. Window-to-wall ratio

Window-to-wall ratio (WWR) is defined as the portion of an exterior wall area occupied by windows, which influences multiple building at- tributes (Troup et al., 2019).Window size is an influential factor in environmental impacts related to material use (Troup et al., 2019). Specifically, window size, type of glass, and the number of glass panes have a significant impact on the embodied energy of windows (Azari and Abbasabadi, 2018). WWR could directly impact embodied energy in the manufacturing of envelopes, or indirectly through changes in the HVAC (heating, ventilation, and air conditioning) and lighting systems (Phil- lips et al., 2020).

WWR is identified as a critical design variable because it impacts ventilation and the amount of daylight and heat gains (Lam et al., 2010). Research attention was paid to two categories: (1) energy consumption along with the change in WWR, and (2) impacts of WWR in different orientations. Susorova et al. (2013) found that WWR was one determi- nant factor in energy consumption of deep rooms in hot climates, as there was a low demand for artificial lighting in the large window area. In cold climates, large window area contributes to energy consumption most in shallow rooms, because of great heat loss (Susorova et al., 2013). Maltais and Gosselin (2017) stated that WWR for the west and south façades was found to be influential in Canada because the two façades collect the most amount of solar radiation. Feng et al. (2017) mentioned that increase in east WWR from 10% to 30% could increase heat gain rate by 264.59W/h and heat loss rate by 16.21W/h, while the same increase in south WWR resulted in 72.64W/h increase in heat gain rate

and 3.05W/h increase in heat loss rate. Therefore, they concluded that east (west) > south > north was the order of impact in cold regions of China (Feng et al., 2017). In Lithuania, 20% is the optimal WWR for south, east and west oriented façades of office buildings, and 20%–40% for the north could improve the energy efficiency (Motuziene and Juo-

dis, 2010). There are variations in recommended fenestration of façades. For instance, window area lowering than 20% of heated floor area is acceptable in Norway, while appropriate WWR is calculated based on dimensions of the rooms in Canada (Motuziene and Juodis, 2010).

- 1. Number of floors

Along with the rapid population growth, there is a trend to build tall buildings to house more people (Resch et al., 2016). The public are often sceptical for the sustainability of tall buildings, because of a greater amount of materials needed for high-rise buildings than the low-rise (Foraboschi et al., 2014). Specifically, a strong foundation to with- stand extra loads and a strong structure to resist the wind load are required for high-rise buildings, which consume more energy-intensive materials that generate great carbon emissions (such as steel) (Resch et al., 2016). Therefore, the increase in building height could increase

embodied energy of a building, with greater heat being transferred to the environment (Resch et al., 2016). Foraboschi et al. (2014) observed an exponential growth in embodied energy with the increase in the number of stories. Compared to low-rise office buildings, about 60% more embodied energy per unit gross floor area of a high-rise building (Treloar et al., 2001).A limited number of studies assessed the rela- tionship between energy consumption and the number of floors. Resch et al. (2016) observed a range of stories that could achieve significant energy savings. Building lifetime (which is related to annual embodied energy) and population (which is related to transportation energy) are two determinants for the range. The optimal number of buildings with low population (10 thousand) and low lifetime (40 years) is 7, while 26 storeys are optimal for a building with 10 million people and 150-year

lifetime (Resch et al., 2016). Specifically, the optimal number of floors was determined by balancing residents’ transportation energy and a building’s embodied energy: a high population could result in high transportation energy and a low building lifetime represents high annual

embodied energy (Resch et al., 2016).

- 1. Building shape

The shape of a building is defined in the early stage with little changes in the following stages (Feng et al., 2021). Building shape can have a significant impact on embodied carbon emissions and operational energy consumption (Asadi et al., 2014; Basbagill et al., 2013). Optimal shape could largely reduce the environmental impacts of a building. For instance, the embodied environmental impacts of a baseline design could be reduced by 40% by selecting an environmentally preferred building shape (Basbagill et al., 2013).

Various shapes were studied in previous literature for finding out the most suitable building shapes in terms of energy efficiency. For example, Feng et al. (2021) studied 7 building shapes: the rectangle, trapezoid, cross, T-shape, U-shape, L-shape, and H-shape and found that trapezoids and rectangles were the most suitable shapes for benchmark residential buildings in America. In another study by Asadi et al. (2014), a com- parison was carried out on the energy performance of 7 building shapes, including rectangle, H-shape, L-shaped, rectangle minus corner, trian- gle, T-shape, and U-shape. H-shape building was found to consume the largest amount of energy (123.7 kWh/m2), because of more energy loss caused by more surface areas connected with the exterior, while rect- angular shape building had the least energy used (Asadi et al., 2014). However, T-shape buildings were observed to have a higher level of energy demands than other types of shapes (rectangle, H-shape, L-shape, rectangle minus corner, triangle, and U-shape) (Mottahedi et al., 2015). AlAnzi et al. (2009) investigated effects of different building shapes (rectangular-shape, L-shape, T-shape, cross-shape, H-shape, U-shape and cut-shape) on energy use in Kuwait. The difference in the impacts of different building shapes on total energy use strongly correlated to other three factors: the relative compactness, WWR and glazing type (AlAnzi et al., 2009). Energy consumption is a result of interactions among multiple factors. Therefore, to determine the optimal building shape, impacts of each factor should be fully understood and assessed (Resch et al., 2016).

- 1. Façade type

Facade refers to the vertical face of a building envelope, such as the exterior wall. It is an important component in sustainable design and energy performance because it causes the greatest energy loss in a building (Krsti´c-Furundˇzi´c et al., 2019). Traditional façade is notorious for its thermal comfort, natural ventilation, and glare, which stimulates the utilisation of new techniques and devices, including shading devices and colour glass (Shameri et al., 2011). It is of great importance to develop a more energy-efficient façade. Energy-efficient façade could create a comfortable internal environment with minimal energy consumed (Aksamija and Peters, 2017). In addition, it could lower carbon emissions during operational phase of a building (Ihara et al., 2015).

Opaque and glazed are two types of façades. Specifically, opaque façades are featured for better insulation and retention, because they are composed of solid materials, while glazed facades mainly contain transparent glazing materials with more access to daylight (Aksamija and Peters, 2017). The energy performance of a highly glazed building is questioned, for the high demand of cooling and heating (Poirazis et al., 2008). Energy use of highly glazed buildings is closely associated with the external environment, such as building shape and orientation (Poirazis et al., 2008). In addition, façade features, including exterior wall type, glazing type, and shading type, should be taken into consid- eration, because they can strongly impact the energy consumption of a building (Yang and Choi, 2015).

- 1. Floor area

Along with the development in economy and improvement in living quality, there is an increase in demand for residential buildings with larger floor areas (Hu et al., 2017). Few studies evaluated the relation- ship between embodied environmental impacts and floor areas. The impacts of floor area on the average global warming potential per floor area were examined and the results revealed that an increase in the floor area could decrease the impact per floor area when the floor area ex-

ceeds 3200 m2, while the change for floor areas under 3200 m2 was less

pronounced (Feng et al., 2019).

It was observed that larger homes might result in an increase in total electricity use for heating, cooling, and lighting, as more capacity and energy required for increased floor areas, while the energy consumed per unit floor area might stay unchanged (Hu et al., 2017). Energy use is the interactions of multiple factors. Aside from the increase in floor area, inclement weather conditions, the increase in the capacity of electronic appliances, increased demand for hot water and widespread use of culinary electronics also contributed to the increase in electricity use (Hu et al., 2017).

Some previous studies assessed the energy use of buildings by considering combination of floor areas with other factors. Krarti et al. (2005) simulated the relationship between perimeter to floor area and lighting energy. Perimeter to total floor area ratio refers to daylight area to total building floor area, and its increase could reduce lighting energy (Krarti et al., 2005). Yohanis et al. (2008) found a strong correlation between electricity use and floor area, while the average consumption of energy per floor area was the same for different types of houses. In addition, changes in household applications, number of occupants, occupation of homes during daytime and number of rooms could impact the daily electricity consumption per unit floor area (Yohanis et al., 2008).

- 1. Floor-to-floor height

Floor-to-floor height of a building affects the building’s solar gains and corresponding energy demands for heating and lighting (Wang

et al., 2021). Moreover, floor-to-floor height also has a great influence on a building’s embodied impacts as the height is related to the mass and the amount of material for creating the building (Feng et al., 2019).

A decrease in floor-to-floor height could reduce the energy demand of buildings (Ghafari et al., 2018; Wang et al., 2021). Specifically, each 10 cm reduction in floor-to-floor height could reduce the use of heating energy by 1% (Ghafari et al., 2018). In June, the heating energy con- sumption increases from 12 to 15.51 kWh along with the increase in floor-to-floor height from 3 to 4 m (Ghafari et al., 2018). Despite the contribution of low floor-to-floor heights to energy saving, variations in floor-to-floor height could cause the difference in daylight levels (Mir- rahimi et al., 2012). The average illuminance of a building decreases with decreasing floor-to-floor height (Mirrahimi et al., 2012). Moreover, there is a direct impact of floor-to-floor height on the internal environ- ment temperature and a large floor-to-floor height could prevent resi- dents from hot air layers (Guimar˜aes et al., 2013).

1. Influence of design variables in detailed design stages

The environmental impacts (carbon emissions and energy use) of four detailed design variables over a building’s life cycle are presented in this subsection.

- 1. Types of building components

Assessment of building components is indispensable when evalu- ating environmental performance of a whole building (Islam et al., 2015). There are five building components above the ground in archi- tectural design, namely walls, floors, ceilings, roofs, and windows and doors (Cang et al., 2020).Walls take up the largest components in a building, which could form a pathway for heat transmission and pro- mote heat radiation in cold environments (Omrany et al., 2016). The thermal performance of walls is substantially influenced by heat ca- pacity. Thermal conductivity is very crucial because proper wall types could reduce energy use for heating and cooling (Omrany et al., 2016). Islam et al. (2015) evaluated the environmental impacts and costs of 19 house designs, including the Base House and 18 houses with alternative walls. A multi-objective optimization approach was adopted to measure four low-carbon eco-innovatory categories and one life cycle cost cate- gory. An up to 20% decrease in the impacts of each low carbon eco-innovatory category could be achieved by the optimal house design (insulated weatherboard with a mixed floor and skillion flat roofing), without increasing life cycle cost (Islam et al., 2015). Moreover, the energy-efficiency performance of three wall types (i.e., light timber frame, aerated autoclaved concrete and masonry brick walls) were assessed and compared (Maoduˇs et al., 2016). The assessment results indicated that masonry brick walls performed better than the other two types, which required the least heating energy. In addition, light timber-frame walls lack thermal mass and resulted in large and cooling energy demand (Maoduˇs et al., 2016). Another four different types of walls (i.e., Trombe walls, autoclaved aerated concrete walls, double skin walls, and green walls) were examined in terms of energy efficiency (Omrany et al., 2016). Trombe walls could significantly decrease the energy consumption of a building (Omrany et al., 2016). Nevertheless, the use of autoclaved aerated concrete and double skin walls were recognized as promising measures due to their significant advantages in minimizing energy consumption, easing the process of installation and transportation, and having satisfactory thermal performance and acoustic properties (Omrany et al., 2016). In referring to the comparison to curtain walls, opaque walls were found outperformed transparent curtain walls in reducing heat transfer and optimizing energy use (Aksamija and Peters, 2017).

Energy performance and indoor thermal environments caused by the other four components (i.e., floors, ceilings, roofs, and windows and doors) also attracted increasing academic attention. An uninsulated

floor is capable to cool the indoor air temperature by 0.2 MJ/m2 a day

(Staszczuk et al., 2017). In cold regions, thermal radiant ceiling heating system could serve as an energy-efficient air conditioning system (Liao et al., 2022). Specifically, the average heating index per floor area was

19.63 W/m2, which is approximately 30% lower than the recommended

value of 28 W/m2 (Liao et al., 2022). Horizontal ceiling panels have radiant effects, which negatively influence heat storage capacity (Niu et al., 1995). Cooled ceiling panels could be installed in hot and humid regions, which create pathways for water flows and increase heat extraction with reduced fan energy (Niu et al., 1995). Novoselac and Srebric (2002) found that insulation between cooled-ceiling panels and ceilings could influence cooling capacity and energy consumption. A 24% increase in cooling capacity and greater energy consumption was observed for uninsulated panels with air circulation, compared to insulated panels (Novoselac and Srebric, 2002). In addition, window types could affect thermal performance and less heat loss was observed in windows with double-glazed plastic sash than in double-glazed wooden sash (Koçlar Oral, 2000). Moreover, five types of roofing sys- tems (i.e., reinforced concrete slab roof, stabilised mud block filler slab

roof, brick panel roof, reinforced concrete ribbed slab roof and Ferro concrete tile roof) were examined from the perspective of energy per- formance (Venkatarama Reddy and Jagadish, 2003). Compared to reinforced concrete slab roofs, masonry vault roofs were proven to be more energy efficient (Venkatarama Reddy and Jagadish, 2003). A 20% reduction in energy content could be achieved by using stabilised mud blocks in reinforced concrete slab roofing systems (Venkatarama Reddy and Jagadish, 2003).

- 1. Specifications of building components

Previous research identified multiple measures which could affect the environmental impacts of a building, including window size, the thickness of the ceiling concrete slab, and the thickness of floor (Bas- bagill et al., 2013; Yigit and Ozorhon, 2018).

Window physically connects the exterior, and its size is one deter- minant for the annual energy use of cooling and heating (Troup et al., 2019). Increasing window size allows rise of ventilated air, because of the increase in the amount of air entering the room (Elghamry and Hassan, 2020). Total energy use decreases along with the increase in

window size, while building energy use significantly increases when window size exceeds 1.62 m2 (Delgarm et al., 2018). Specifically, win- dow size has greater impact on annual cooling than heating and lighting

(Delgarm et al., 2018). The thickness of building components is one

dimension that should be considered during the building design process. The thickness changes in window assembly could reduce the building’s embodied impact by 0–0.78% (Basbagill et al., 2013). In addition, the choice of window glazing is critical for daylight and energy saving,

while the selection could be complicated (Hee et al., 2015). Triple-glazed windows performed better than the double-glazed window in terms of energy efficiency for various WWR and orientation combi- nations (Hee et al., 2015). As described in section 3.2.2, WWR, orien- tations, and climate background should also be considered in window design. However, an environmentally optimized window design does not imply that it is economical. For instance, although clear glass was found to have the shortest payback period, other types of glazing had a better performance in energy saving (Hee et al., 2015). Moreover, adjusting the thickness of the concrete floor slab could affect energy consumption. Staszczuk and Kuczyn´ski (2019) found that increasing the thickness of concrete floor slab from 8 cm to 18 cm could improve the thermal capacity from 2.64 MJ/K to 6.24 MJ/K and decrease total en- ergy use by 17.0%. Furthermore, the thickness changes of ceilings can potentially lower the embodied environmental impacts of buildings. A range from 3.82% to 6.98% of embodied impacts of a building could be reduced by solely changing the thickness of ceilings (Basbagill et al., 2013).

- 1. Types of building materials

Different types of materials contain different levels of embodied energy. Table 1 presents embodied energy in different types of materials. Using materials with high embodied energy may lead to high future energy consumption to meet heating, ventilation, and air conditioning needs (Zabalza Bribi´an et al., 2011). Energy consumption and environ- mental impacts could be lowered by selecting environmentally accepted materials (Maoduˇs et al., 2016). For instance, wood structures have minimal embodied energy and CO₂ emissions than concrete, steel, and brick structures, because timber has much less embodied energy than other materials (Maoduˇs et al., 2016). Portland cement is one widely consumed material, and it consumes a great amount of energy in crushing and grinding the clinker (Venkatarama Reddy and Jagadish, 2003). Up to 1.93 MJ-Eq/kg energy could be saved by using concrete

Table 1

tiles, compared to ceramic tiles (Zabalza Bribi´an et al., 2011). Energy saving could be achieved by using renewable energy or recycled prod- ucts (Huberman and Pearlmutter, 2008). For instance, multiple forms of renewable energy are obtained from the sun, wind, ocean, hydropower, etc., and their use could achieve a significant reduction in the energy consumption of a building (Chel and Kaushik, 2018). In addition, recycled concrete contains recycled aggregate from construction and demolition waste, which is one alternative material to virgin concrete. The use of recycled concrete could reduce the consumption of natural resources and solve the problem of landfilling (Kisku et al., 2017). Recycled concrete was found to have lower embodied energy compared to virgin concrete with the same mix design (Xing et al., 2022). Since cement has the greatest environmental impact, replacing cement with cementitious materials (such as fly ash and silica fume) in concrete could reduce the environmental impacts of concrete (Xing et al., 2022). The impacts of a particular material on energy use could be different (or even contradictory) in different phases of a building (Huberman and Pearl- mutter, 2008). For instance, a high insulation value could save energy at the operational phase, while it can generate high embodied energy costs (Huberman and Pearlmutter, 2008).

Installation of insulation materials on the building envelope is regarded as the most effective method to decrease the energy use of a building since heating and cooling use the largest amount of energy (Zhao and Li, 2022). The main objective for using insulation materials is to control heat transfer and therefore enhance thermal performance and energy efficiency (Wi et al., 2021a). Insulation materials could contribute to energy conservation by decreasing reliance on heating, ventilation, and air conditioning system (Hung Anh and Pa´sztory, 2021). A great collection of previous studies aimed to improve the thermal resistance (or decrease thermal conductivity) of insulation materials (Wi et al., 2021a). Multiple factors, including temperature, moisture con- tent, and bulk density were found to be influential on thermal conduc- tivity (Hung Anh and P´asztory, 2021). Table 2 presents the thermal conductivity of different insulation materials. Specifically, insulation materials could be classified into three categories based on their source and composition: organic insulation materials (such as expanded poly- styrene, extruded polystyrene, and phenolic foam), inorganic insulation materials (including glass wool and rock wool), and advanced insulation materials (including aerogel insulation) (Zhao and Li, 2022). In Europe, inorganic fibrous materials occupy 60% of the market for insulation materials, while organic materials take up 27% of the market (Villasmil et al., 2019). Conventional materials (including extruded polystyrene and expanded polystyrene) are popular for their low thermal conduc-

tivity and low cost (Hung Anh and P´asztory, 2021). A well-insulated house could contribute to 50%–90% energy saving (Aditya et al., 2017). In Australia, 20–30% heat loss in walls and 30–40% loss in

ceilings could be avoided, if the house is insulated according to Australian Standard AS2627 (Aditya et al., 2017). In addition, Zhao and Li (2022) observed the energy consumption of insulation materials containing agricultural solid waste and found that this material had less annual power consumption than conventional insulation materials. However, in order to improve heat resistance of insulation materials, some chemicals have been added which might release pollutants, in- crease hazards and cause health problems (Wi et al., 2021b). For instance, one expanded polystyrene product was found to emit formal- dehyde, and future research efforts could be spent on eco-friendly insulating materials (Wi et al., 2021b).

Along with the increase in the use of insulation materials, their embodied energy should be emphasized (Kumar et al., 2020). Although there is a wide range of estimates on the embodied energy of a single

Energy in different types of materials (Goggins et al., 2010; Lenzen and Treloar, 2002; Venkatarama Reddy and Jagadish, 2003).

Types of materials	Lime-pozzolana	Steel	Aluminium	Glass	Hydrated lime	Wood	30 MPa Concrete
Thermal energy (MJ/kg)	5.85	42.0	236.8	25.8	5.63	1	1.08

Table 2

Embodied energy, global warming potential, and thermal conductivity of different insulation materials.

Categories Organic Inorganic Advanced

Products Polyurethane Extruded Polystyrene Expanded Polystyrene Glass wool Stone wool Aerogel Embodied energy (MJ/f.u.) 99.63 127.31 44–78 16–31 21–66 251–372

Global warming potential (kg CO2eq/f.u.) 6.51 13.22 1.9–3.5 0.6–1.2 1.4–4.2 11.6–18.7

Thermal conductivity (W/(m⋅K)) 0.022–0.040 0.031–0.036 0.031–0.037 0.31–0.45 0.33–0.40 0.015–0.028

References Schiavoni et al. (2016) Grazieschi et al. (2021)

type of insulation, the embodied energy of insulating materials is still a point of contention (Azari and Abbasabadi, 2018). Table 2 describes embodied energy and the global warming potential of different types of insulation materials. Compared to organic materials, inorganic insu- lation materials exhibit lower embodied environmental impacts, but with higher thermal conductivity (Azari and Abbasabadi, 2018). While aerogel insulation could reduce energy use during the operation phase, its embodied energy is significantly higher than other types of materials (Kumar et al., 2020). However, the significance of embodied energy was underestimated (Crawford et al., 2016). The period for embodied energy of insulation materials to be offset by energy savings during operation might be longer than anticipated (Crawford et al., 2016).

- 1. Thickness of building materials

Heat loss in walls, ceilings, and floorings could take up 53% of a building’s total heat loss (Çomakli and Yüksel, 2004). Heat insulation could be installed in these places to reduce heat loss in a building. The

thickness of insulation materials is associated with the potential to save energy for heating and reduce CO₂ emissions (Çomakli and Yüksel, 2004). The requirements for minimum insulation thickness are deter- mined by multiple factors, including climate, location, and shape factors of a building (Feng et al., 2019). In Erzurum Turkey, the optimal thickness of insulation is 10 cm, which could contribute to a 27% reduction in the amount of fuel used than 4 cm of insulation (Çomakli and Yüksel, 2004). Regarding the most commonly used types, the thermal conductivity decreases along with the increase in insulation thickness, but there is an increase in cost which might surpass the eco- nomic benefits of energy savings (Aditya et al., 2017). Van Gulck et al. (2020) proved that increase in insulation thickness could contribute to the environment, while the environmental optimal thickness did not equal to the financial optimal thickness. For instance, 14 cm with a

U-value of 0.25 W/m2K was found to be the financial optimal thickness

for standard external thermal insulation composite systems, but the optimal thickness for the environment exceeds 40 cm with a U-value of

0.09 W/m2K (Van Gulck et al., 2020).

3.4. Influence of design variables in construction design stages

The quality of interior finishes (including floor and wall interior

finishes) is one determinant to the thermal and humidity of the indoor environment which is closely associated with residents’ health and buildings’ energy demands (Bako and Jusan, 2012). Using functional materials for interior finishing could buffer heat and moisture (Shi et al.,

2019). It is of great importance to properly select finishing materials for the interior environment because the choice could bring about envi- ronmental, economic, and social benefits (Zhang et al., 2019). Specif- ically, the choice of finishing materials should consider their efficiency, durability, and maintenance requirements, because the selection is related to initial, operation, and maintenance costs during the life cycle of materials (Dixit et al., 2019). Boarding materials (including gypsum boards) and painting materials (such as diatomite) are the two main types of materials used in interior finishing (Shi et al., 2019). Shi et al. (2019) found that additional microencapsulated phase change material (MPCM) contents in the gypsum board could enhance the heat capacity and absorption ability of materials. Kim et al. (2021) observed the use of artificial stone from recycled bricks as an interior finishing material and

found that adding a certain type of carbon material could improve the thermal performance of artificial stone. However, pollutant emissions from finishing materials, including volatile organic compounds, could

negatively influence the indoor environment and disrupt residents’

health (Dixit et al., 2019). Wi et al. (2021a) mentioned that the emission of total volatile organic compounds from interior finishing materials,

such as flooring and wallpaper, should be less than 0.10 mg/m2⋅h.

However, the impacts of design variables during the construction stage received little attention, because changes in this phase could result in a significant cost, but without the great potential to reduce carbon emis- sions. In addition, users usually have preferences for interior designs, and it is difficult to define a general design formula, which makes the evaluation challenge.

Recommendations for future work

Above research results have identified design variables that affect the life-cycle environmental impacts of a building throughout the whole design processes. Based on the research findings, this section discusses future perspectives on building design variables for creating environ- mentally friendly design solutions.

1. Examining predominant design variables

There are numerous design variables affecting the energy con- sumption and environmental emissions of buildings in each design stage throughout the design process. Thousands of design alternatives can be generated by varying the identified design variables. However, making design decisions based on the environmental impacts of design alter- natives appears to be impracticable since it is difficult to conduct an environmental impact assessment for all potential design alternatives. A possible solution is focusing on decisions related to design variables that have a great influence on the environmental performance of a building. By assigning proper values for the most influential design variables, an environmentally preferred building design may be created. Therefore, it is significant to estimate the relative importance of each design variable in building environmental impacts.

Recently, research on the importance of design variables has received great attention from researchers. In this regard, sensitivity analysis, which refers to a statistical procedure of determining the im- pacts of input variables of a model on its output (Saltelli et al., 1999), has been widely adopted for showing how building environmental impacts depend on the design variables. Local and global sensitivity analysis were often conducted to prioritise design variables in terms of their influence on building environmental impacts. For example, Bre et al. (2016) explored the ranking of design variables including building orientation, window size, and the specifications of the wall from the perspective of their impacts on energy demands by employing the one-factor-at-a-time method, a local sensitivity analysis method. In another study by Silva and Ghisi (2020), 21 design variables were prioritized in line with the levels of their influence on embodied envi- ronmental impacts by using Morris elementary effects methods (one of the most popular global sensitivity analysis methods). However, there are two problems in carrying out sensitivity analysis on design variables, which may lead to misleading information on the importance of design variables. First, the sensitivity analysis did not consider the different and

sequential stages of a real design process during which a limited number of variables are chosen at a time. In previous work, all design variables were often included together at the same time for analysis (Basbagill et al., 2013; Wang et al., 2020). The already fixed design variables were not separated from those to be determined in later design stages. As a result, the interaction effects between design variables that should not appear at the same stage, such as the interaction between window-to-wall ratio and the specifications of glazing, would largely lower the effectiveness of sensitivity analysis results. Accordingly, the priorities of design variables in line with the sensitivity analysis results are less effective. Moreover, the lack of life cycle inventories in some building materials or components may lower the accuracy of the outputs in the sensitivity analysis. The shortage in information associated with material quantities, embodied coefficients and the service life of build- ing components leads to large uncertainties in the accuracy of the environmental impacts of a building (Ansah et al., 2021). The un- certainties in environmental impacts would cause misleading results on

how design variables affect building environmental impacts, thereby failing to identify the ‘true’ predominant design variables. Therefore, a promising research direction is to determine predominant design vari- ables affecting the environmental impacts in each design stage with the consideration of uncertainties in building environmental impact

assessment.

1. Developing standards to determine value domains of design variables in terms of building environmental impacts

Results revealed that various values assigned for design variables lead to different environmental consequences. For instance, the envi- ronmental impacts of a building may greatly vary by modifying the specifications and thicknesses of building components and materials in detailed design stages. Selecting appropriate values for design variables has great potential to reduce the environmental consequences and create energy-efficient and low-carbon building designs. In this case, the prerequisite is to determine the value domain for each design variable from the perspective of the environmental impacts of a building over its life cycle.

There are two types of standards, codes, or regulations related to design variables and environmental impacts. The first type is the ar- chitecture building design codes, regulations or technical standards in which the value domains of several design variables are stated according to the structural stability, thermal performance and other characteristics of a building. For example, the floor-to-floor height of a residential building should be lower than 2.8m while the height of living room and bedroom should be higher than 2.4m according to the residential building design codes in China (MOHURD, 2011). However, the value domains of design variables stated in architecture building design codes, regulations or technical standards are heavily dependent on the climate conditions, the construction sites and the design approaches. For example, in referring to the orientation of a building, the possible rotation angles are associated with prevailing wind direction of the building project site. In other words, the value domains of design vari- ables cannot be generalized to building projects in other climates, lo- cations and situations. The second type is related to the rating systems or regulations for sustainable buildings, such as BREEAM in the United Kingdom, LEED in the United States, and Green Star in Australia, and CGBL in China (Zuo and Zhao, 2014). These rating systems are similar in terms of the structures and contents, e.g., covering social, economic and environmental aspects, presenting a number of credits (each with some points) in each category and including different project types. In refer- ring to the environmental aspects of building design, the environmental performance targets for a building project should be stated for the nominated building. For instance, in Green Star, the basic functions, operations, and maintenance of the building, the targets for the energy and water consumption, and the list of the design parameters should be described. However, the values/value domains of design variables are

not specified in the rating systems or regulations. There is much room to explore new codes, regulations and standards related to the value domain of design variables from the perspective of lifecycle environ- mental impacts of a building.

1. Focusing on design variables affecting lifecycle environmental impacts

Results showed that some design variables affect embodied envi- ronmental impacts (embodied carbon emissions or energy use) such as the finishing materials for walls and floors, some variables, such as insulation thickness of floor, heat transfer coefficient of walls, and U- value of glazing are related to operational environmental impacts (operational carbon emissions or energy use), while other variables, such as window-to-wall ratios, building aspect ratios concern the life- cycle environmental impacts of a building. In this context, there are two cases for the optimization of building design in terms of the selection of design variables. First, if design variables that are intended to be opti- mized are only related to the embodied environmental impacts or operational environmental impacts, by focusing on these design vari- ables the minimum environmental impacts of a building over its life cycle could be achieved. However, if design variables concern the total life-cycle environmental impacts and have a contrary influence on the embodied and operational impact, the trade-off between embodied and operational impacts of a building needs to be taken into consideration for the optimization of building design. For example, high-value insu- lation materials may save operational energy but generate high embodied energy, and thus selecting insulation materials needs to be cautious to find out the trade-off between embodied impacts and oper- ational impacts. Therefore, further research could focus on design var- iables affecting total life-cycle environmental impacts to examine the positive or negative influence on the embodied and operational impact. Moreover, for design variables that have inconsistent influence direction on embodied and environmental impacts of a building, more attention can be paid to how to determine the design variables for achieving the minimum total life-cycle environmental impacts.

Conclusions

This paper examined the design variable that may have an influence on the environmental impacts of a building over its life cycle by con- ducting a critical review. Fifty papers between 2010 and 2022 were selected for further analysis. The results show that eight design variables

in early design stage were identified to have an influence on a building’s

life-cycle environmental impacts. They are (1) building aspect ratio, (2) window-to-wall ratio, (3) shading area, (4) building orientation, (5) number of floors, (6) building shape, (7) floor area and (8) floor-to-floor height. There are four kinds of design variables linking with the envi- ronmental impacts of a building in detailed design stages: (1) types of building components, (2) sizes of building components, (3) types of building materials, and (4) thickness of building materials. The types and thicknesses of finishing materials are closely related to the envi- ronmental impacts of a building in construction design stage.

Since there is an increasing concern regarding the influence of design variables on environmental impacts of buildings, future research efforts could be put into: (1) examining predominant design variables in each design stage with the consideration of uncertainties in environmental impact assessment, (2) developing standards to determine value do- mains of design variables in terms of building environmental impacts, and (3) focusing on design variables affecting life-cycle environmental impacts. The findings of this paper could help better understand the relationships between design variables and building environmental impacts. Moreover, by focusing on the lists of design variables presented in this study, it will help create environmental preferred design solu- tions. Nevertheless, it is appreciated that peer-reviewed articles referred to in this study may not contain all related publication works. Non-

traditional research outputs and grey literature in the building design field are not included in this study. It is recommended for future research to include articles retrieved from these sources for further analysis.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

No data was used for the research described in the article.

Acknowledgements

The authors wish to acknowledge the Australian Research Council (ARC) Discovery Projects’ financial support under grant number DP190100559.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi. org/10.1016/j.jclepro.2023.135921.

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组合 1

Shaoxiong Li 1 , Le Liu 1 and Changhai Peng 1,2,*

Introduction

Performance-Oriented Design and Optimization

Retrospective Analysis

Industry Dividends and Potential Challenges

Conclusions and Future Work

References

Design variables affecting the environmental impacts of buildings: A

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