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Building and Environment 250 (2024) 111184
Available online 11 January 2024
0360-1323/© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Multi-criteria design methods in façade engineering: State-of-the-art and
future trends
Simona Bianchi
*
, Charalampos Andriotis , Tillman Klein , Mauro Overend
Faculty of Architecture and the Built Environment, Delft University of Technology, Julianalaan 134, 2628 BL, Delft, Netherlands
ARTICLE INFO
Keywords:
Façade design
Performance-based
Decision-making
Multi-criteria
Multi-objective
Optimization
ABSTRACT
Façade engineering is facing an era of extraordinary challenge to meet the surge in demand for buildings that are
environmentally sustainable and enhance occupant wellbeing. Facades, also known as building envelopes, play a
major role in the resource-efciency of buildings and the quality of its indoor environment. Consequently, the
development of effective design approaches is crucial for generating appropriate façade solutions. Façade design
is complex and multi-disciplinary involving several and oftentimes conicting performance criteria. Systematic
and holistic design procedures are, therefore, required to achieve optimal trade-offs. Over the last decades, re-
searchers in this eld have used computational tools and power to address this challenging problem within the
context of multi-criteria design approaches. This paper reviews the existing research in this eld, and presents
the state-of-the-art review from simple to advanced decision-making procedures currently used at the early
design stages, where decisions have a disproportionally large impact on the façade performance. The paper
provides a complete description of the design variables and objectives typically involved. Alternative multi-
criteria design methodologies regarding discrete decisions and automated optimization are reviewed, each
with salient pros/cons, and overall conclusions are drawn. Finally, the paper discusses ongoing trends and
research needs, namely, the development of uncertainty-based procedures to enable more informed decision-
making; the inclusion of structural/seismic safety considerations in the design process to achieve higher socio-
economic benets; the integration of smart building information modeling and processing technologies to
facilitate smarter design decisions; and the adoption of integrated design approaches to promote climate-
adaptive solutions that enhance resilience.
1. Introduction
Facades, also known as building envelopes, act as lters between the
building’s interior and exterior environments. They are connected to the
main load-bearing structure and provide the external architectural
expression of the building. Fig. 1 provides an overview of the primary
typologies currently employed. Facades can be classied based on the
panel modularity and connection details. Panel modularity includes
mono-panels, as in inll walls, and multi-panels, comprising both ver-
tical and/or horizontal panels as in curtain and cladding walls.
Connection details refer to how the panel is connected to the primary
structure, through interface elements such as mortar lling for masonry
inlls, continuous elements for timber/steel inlls, or discrete bracketry
for curtain and cladding walls. Alternative systems have also emerged
due to the development of different materials, construction processes
and architectural designs. Moreover, to meet ever stricter energy
performance requirements [1,2], facades have become integrated
multi-material, multi-functional components, often with some inte-
grated adaptive features.
Facades are, therefore, complex systems designed for several per-
formance criteria associated with their multiple functions. They have
signicant impact on the functional and economic aspects of the entire
building (accounting for up to the 30 % of the total cost of a building [3])
and play a paramount role in its aesthetics. Their ultimate functional
aim is to provide an indoor environment, which is safe and comfortable
for building occupants in an aesthetically pleasing and resource-efcient
manner. In doing so, facades control (i) heat and mass transfer, water,
acoustic and light transmission between the inner and outer environ-
ment which have a direct impact on the operational energy demand and
occupant comfort; and (ii) resist wind, impact, earthquake, re and
other actions which have a direct impact on embodied energy and
occupant safety. This leads to multiple and conicting objectives during
the design process. For instance, a high window-to-wall ratio reduces
* Corresponding author.
E-mail address: S.Bianchi@tudelft.nl (S. Bianchi).
Contents lists available at ScienceDirect
Building and Environment
journal homepage: www.elsevier.com/locate/buildenv
https://doi.org/10.1016/j.buildenv.2024.111184
Received 29 September 2023; Received in revised form 14 December 2023; Accepted 8 January 2024
Building and Environment 250 (2024) 111184
2
articial lighting demand but may cause overheating during the cooling
season due to increased solar heat gain. Similarly, enlarging internal
gaps enhances façade dynamic behavior during earthquakes but may
compromise acoustic performance.
Façade design consists of a whole process characterized by
increasing levels of detail [4]. This process begins with a conceptual
design, which involves exploring various design options. The next stage
involves creating a detailed façade design, that includes the selection of
materials and buildup, as well as accurate evaluations of the overall
performance and cost. The nal stage is the technical design, which
involves creating a detailed production plan that outlines the specic
steps required to bring the façade design to life. The design process is
multi-objective and considers many design variables, each of them likely
to affect several performance indicators. Therefore, the different design
variables should be properly combined to achieve the optimal trade-offs
among all the performance indicators. This makes design decisions
difcult, and the difculty is further increased if the uncertainties of the
design variables are taken into account and a proper quantication of
the relative impacts of the design variables is needed [5,6].
The complex and multi-disciplinary design process has been plagued
by inefcient design and operational stages in the past, leading to an
increasing negative impact on the environment. To address this, the
development of effective design tools and methods has become crucial in
targeting higher levels of energy efciency for building envelopes and
fullling current sustainability requirements. Façade engineering is
therefore facing an extraordinary challenging era to achieve this goal.
Early research efforts focused on the development of computer-aided
design tools to automate the drawing production by generating ef-
cient geometrical building models (since late 1960). This was followed
by parametric and performance-based design procedures that have
emerged (since late 1980) as an integrated approach to combine several
design parameters from the early design stage [7]. Parametric design,
Abbreviations
AHP Analytic Hierarchy Process
AI Articial Intelligence
ANN Articial Neural Networks
ANP Analytic Network Process
BIM Building Information Modelling
COPRAS COmplex PRoportional Assessment
DE Differential Evolution
DRL Deep Reinforcement Learning
ELECTRE ELimination Et Choix Traduisant la REalit´
e
GA Genetic Algorithm
GAN Generative Adversarial Network
NSGA-II Non-dominated Sorting Genetic Algorithm
MADM Multi-Attribute Decision-Making
MAS Multi Agent Systems
MCDM Multi-Criteria Decision-Making
MODM Multi-Objective Decision Making
PROMETHEE Preference Ranking Organization Method for
Enrichment Evaluation
PSO Particle Swarm Optimization
SPEA-2 Strength Pareto Evolutionary Algorithm
TOPSIS Technique for Order of Preference by Similarity to Ideal
Solution
WASPA Weighted Aggregated Sum Product Assessment
WPM Weighted Product Method
WSM Weighted Sum Method
Fig. 1. Spectrum of façade typologies.
S. Bianchi et al.
Building and Environment 250 (2024) 111184
3
coupled with building simulation tools, streamlines the assessment and
selection of optimal solutions for multi-dimensional problems, estab-
lishing it as the predominant modeling process in façade design.
The rapid advancement of digital technologies has given rise to an
extensive range of computational approaches and techniques available
to designers, enabling them to produce and rationalize design processes
and outputs. Particularly, computational advancements can provide
designers with a more methodological approach to address façade
design problems. Designers are now able to predict the building/façade
behavior, explore the full realm of parameters affecting the models,
manage the conicting design objectives and generate multiple and/or
optimal design alternatives by coupling various parameters with per-
formance metrics. To provide a comprehensive understanding of this
topic, this paper aims to present a systematic state-of-the-art review of
simplied-to-comprehensive methods that integrate computational
performance simulation in the façade design process. A signicant
research effort by a growing number of researchers has been made in
this research eld, particularly in recent years, focusing on the devel-
opment of early-stage design tools, decision-making procedures and
automated optimization, as well as accounting for various design vari-
ables and objectives. This state-of-art review rst provides an overview
of the various façade performance aspects that need to be considered in
the design (Section 2). The paper then presents alternative multi-criteria
design methodologies, regarding discrete and continuous decision
spaces, and related advantages/disadvantages, which might be applied
in façade design problems (Section 3). The paper nally discusses
ongoing research and new perspectives in façade engineering (Section
4).
2. Façade performance criteria
Building facades are subject to various loads and displacements
during their whole life. In addition to imposed and variable loads, either
the external/internal environment or accidental events, such as earth-
quakes or res, cause actions which may affect the functionality of the
building envelope. A façade system should be designed to account for all
the possible impacts on its components and risks threatening its life
cycle performance. This leads to multiple functions described as per-
formance requirements (or criteria) a façade needs to full. Many
authors provide a comprehensive list of performance requirements
[8–11] and these generally fall into three categories: functional (struc-
tural safety, human comfort, durability), environmental (energy and
material efciency) and nancial (cost effectiveness) (Fig. 2).
2.1. Functional: Structural safety
Facades must support their self-weight and allow for differential
deformations caused by moisture, temperature and structural move-
ments, e.g. oor slab deections. Additionally, they need to withstand
environmental loads, such as rain, wind and, for sloped facades, snow.
The structural design is not limited to the principal façade components
(e.g., glass panel and framing for curtain walls), but also includes the
connections between these components as well as the connections be-
tween the façade and the primary load-bearing structure. These con-
nections must safely transfer loads and accommodate necessary
deformation exibility, while providing construction tolerances.
Furthermore, facades must have adequate re resistance capacity to
prevent failure, and ensure safe escape routes and access for rescue
crews during a re. This requires limiting the probability of re prop-
agation and providing proper mechanical resistance, integrity and
insulation against heat, ames, smoke.
Designing building facades also involves addressing man-made (blast
loading) and natural hazards (earthquakes). To prevent serious injuries
to people in the event of an explosion, the glass should absorb and
disperse the impact pressure wave while retaining broken glass and
debris [12]. Additionally, façade design should consider blast-related
pressures, impulse and load duration. During earthquakes, even
low-intensity events can cause functional loss or serious damage to fa-
cades [13]. To reduce vulnerability, seismic demand parameters (ac-
celerations, displacements) should be properly evaluated and
considered in designing construction details and anchorage systems to
the primary structure.
2.2. Functional: Human comfort
Facades create visual and physical connections to the outside and
have major impact on the appearance of the building and the street-
scape. Exterior envelopes provide access to daylight, and have positive
Fig. 2. Main façade performance criteria.
S. Bianchi et al.
Building and Environment 250 (2024) 111184
4
impact on occupant satisfaction and wellbeing. Although predicting the
inuence of façade design on visual comfort and lighting energy use can
be challenging, attaining a well-daylit space is a crucial design objective.
However, daylight can also cause discomfort, e.g. solar glare and very
high luminance reections on display screens, therefore, all of these
factors need to be considered in daylighting building design [14].
Meeting the heating and cooling needs of the occupants, controlling the
building thermal environment, supporting occupant comfort, produc-
tivity and well-being, are other fundamental roles of facades. Further-
more, facades are designed to control natural ventilation and indoor air
quality, e.g. by using proper ventilation devices and strategies along
with minimizing air leakage through the building envelope. Another
important function of the building envelope is noise mitigation, conse-
quently, facades should provide adequate acoustic performance. Build-
ing facades are indeed the primary surfaces upon which the sound from
the external environment is reected and the reduction of noise level
over their fronts is fundamental and must be addressed, e.g. by adopting
alternative façade shapes and materials [15,16].
2.3. Functional: Durability
Facades, as all the building components, have a natural reduction in
performance over time. This is due to their sensitivity to weathering and
consequent degradation, inevitably leading to loss of functionality. The
capacity of a façade to perform its functions during a specied time
period, under the inuence of the actions expected for the building
operation, is captured by the durability and expressed in terms of service
life [17]. Facades must provide an adequate durability by resisting
condensation and water penetration, as well as facilitating the migration
of excess humidity from inside the building to the outside [18]. To
ensure façade durability, the designer must properly account for the
building use, the environment conditions, the shape and details of the
components and the quality of installation. Protective measures and
maintenance operations must be planned to allow the system to main-
tain its performance and aesthetic features.
2.4. Environmental: Energy & material efciency
Due to climate change concerns, the building industry is putting a
major effort in increasing the sustainability level of our built environ-
ment and fullling energy efciency targets. This focus on sustainable
development is crucial to mitigate negative environmental, economic
and social impact on future generations [19]. Facades have a dispro-
portionate impact on the sustainability of buildings, therefore, the use of
natural and manufactured resources throughout the design, construc-
tion and operation phases should be carefully planned during a project.
Facades are able to reduce energy consumption and energy demand
through the optimal use of daylight, allowing natural air circulation,
avoiding moisture transfer and controlling heat transfer. Lifecycle-based
assessment and design methodologies can be utilized to gauge the sus-
tainability of a façade, and numerous tools have been developed to
address this aspect [20]. These imply dening all the environmental
implications during the whole life-cycle encompassing the extraction
and processing of raw materials, the fabrication phase, transportation
and distribution, use and eventually re-use, the storage, recovery and
nal disposal of façade systems.
2.5. Financial: Cost effectiveness
The economic analysis plays a fundamental role when comparing
alternative solutions or exploring the benets of a product. Rather than
focusing solely on investment costs, life-cycle cost analyses must
consider the entire cost spectrum, including design, construction,
operation, maintenance, upgrades and demolition. The design objective
should aim to minimize all these costs. Consequently, the considerable
upfront investment in high-performance facades can be offset by lower
operational and maintenance costs over its lifespan, resulting in more
cost-effective solutions. For example, employing damage-control tech-
nologies to create “earthquake-proof” systems may involve higher initial
costs compared to conventional solutions. However, economically
feasible payback periods can be identied, allowing for the recovery of
invested funds and yielding signicant savings by the end of the service
life [21]. Moreover, long-term cost efciency can be maximized by
integrating various technologies, such as facades embedding air condi-
tioning systems. This may result in a multi-layer and multi-material
high-performance solution, but poses challenges for disassembly and
re-use/recycling at the end of its life [22].
3. Façade design process
Façade design is a complex multi-disciplinary process involving
different stakeholders and domains of expertise. The overall process
consists of the combination of architectural design, execution design and
product design and their overlaps. Architects and engineers are regu-
larly involved in consulting the client during the project development
phase, while the façade builder and system provider try to establish long
term relations with clients and architects or consultants [3]. These re-
lationships are vital for the design and construction of a façade, as they
enable a more collaborative and iterative process that can lead to better
outcomes. The whole process of a traditional route for façade design and
construction are described in detail in the process mapping developed by
Voss et al. [23]. Although an interactive and integrated design approach
is desirable, a traditional sequential design procedure is still employed.
The design evolves through increasing complexity levels which are
intrinsically interdependent: the initial design choices have a signicant
impact on subsequent steps, while later steps play a role in driving the
initial design choices, particularly when it comes to meeting
production-related constraints [24].
Focusing on the architectural design, three main design stages can be
identied: (a) building early design, (b) façade preliminary design, and
(c) façade detailed design (Fig. 3). Before starting the design process, the
designer must decide on the importance of each performance criterion,
since this prioritization will inuence the decisions throughout the
overall process. Project limitations and constraints, e.g. available budget
and material type for the building façade, are identied at the earliest
design stage. A conceptual workow is rstly developed to dene basic
geometrical features, building massing, orientation and performance
criteria for the overall building. This initial stage has a signicant impact
(around 80 % [25]) on all subsequent design decisions and involves the
denition of performance indicators, optimization and performance
prediction. Then, (often several) preliminary design options are devel-
oped to satisfy all the intended design criteria and the most appropriate
design is selected for the project. Finally, a detailed design of the façade
is carried out to determine detailed information for production and
installation. This later stage involves comprehensive assessments
through numerical modelling to study the façade behavior at a
system-level (whole façade) and at local level (members and join-
ts/connections). During the overall process, verications are imple-
mented to check that the proposed design is code-compliant and it
satises project-related design requirements in terms of manufactur-
ability, cost and performance. Moreover, additional considerations need
attention, including accommodating construction alterations, address-
ing durability concerns for both the façade and its connections, opti-
mizing construction processes for efciency, and developing a
comprehensive plan for maintenance, inspection and end-of-life man-
agement of the envelope [26]. This highlights the need for a proper
communication between professionals and decision makers, and the
seamless integration of all façade design stages. Building Information
Modelling (BIM) is nowadays applied to facilitate this integration,
particularly to monitor life-cycle decisions from conception to demoli-
tion, by providing accurate information and three-dimensional visuali-
zations throughout the project.
S. Bianchi et al.
Building and Environment 250 (2024) 111184
5
Within this overall design procedure, selecting the proper façade
system for each project is the most important decision to make. In
addition to project goals, limitations and constraints, the designer must
identify all the key performance design aspects, that is, not only struc-
tural integrity and energy efciency as typically happens, but all the
decisive criteria. The design objective should not merely be to dene a
façade system with acceptable performance levels: instead, the aim
should be to seek for an optimal solution. Since some performance
criteria are conicting with each other, the façade design leads to Pareto
decisions meaning that there is no single solution achieving the highest
score on each of the performance criteria, but one that establishes ef-
cient trade-offs among the criteria. The choice of trade-off will lead a
different (optimal) solution on the Pareto front (see Section 3.2). How-
ever, most designers presently tend to use design methods that are
optimized with respect to only a few objectives, due to a limited un-
derstanding of the complex interactions among different design vari-
ables or due to the prioritization of certain project-related objectives.
However, the goal should be to maximize the overall performance ac-
counting for the interactions and correlations between the design
criteria. To achieve this, it is essential to implement a systematic design
method that can help identify, assess and integrate various performance
criteria.
Given the importance of a holistic design approach, several studies
have focused on developing simplied procedures to guide stakeholders.
One of the early research in this direction was carried out by Ram-
achandran [27], who developed an integrated tool to support façade
design by referring to existing practices and knowledge-based technol-
ogies. Additional research on analytical-based approaches has emerged
in the last decade. Donato et al. [28] developed a parametric and
multi-disciplinary procedure to investigate the relationships between
envelope features and cooling strategies. Kültür et al. [29] proposed a
supporting holistic tool based upon an extensive literature review and
providing the impacts of design decisions on different façade functional
aspects (safety, health-related, well-being). To dene a systematic
approach for energy retrotting, Pracucci et al. [30] developed a simple
multi-criteria decision matrix to support the façade technology selec-
tion. This qualitative method provides an overall score based on the
simple sum of each project requirements to each current market com-
ponents. Focusing on public buildings, Vullo et al. [31] proposed a
conceptual methodology to drive procurement procedures based on the
overall building performance, rather than tenders based on single façade
properties. While it is true that these tools rely on simplied in-
vestigations and/or literature-based information, and may require the
integration of additional façade functions and typologies, they can still
be valuable resources. Designers can save time, reduce labor costs and
minimize expenses associated with time-consuming analyses, stake-
holders can gain awareness of the impact of design variations on
building performance and make more informed decisions from the very
early stage.
Although simplied methods can be useful for certain applications,
more rened procedures should be applied at the early stages of façade
design to achieve high-performance multi-functional systems. To this
end, Multi-Criteria Decision-Making (MCDM) methods have gained
increasing attention in recent years [32]. Building on existing literature,
this section presents a review of the MCDM approaches adopted in
façade engineering, with their potential benets and limitations. MCDM
is branch of operational research that employs techniques and mathe-
matical tools to facilitate the analysis and selection of alternatives based
on pre-selected criteria. MCDM involves Multi-Attribute Decision Mak-
ing (MADM) and Multi-Objective Decision Making (MODM) [33].
MADM concentrates on problems with discrete decision spaces, while
MODM involves mathematical optimization with several competing
objectives to be optimized simultaneously. These performance-based
design methods provide a solution to the challenges especially faced
in the conceptual design stage of building facades.
3.1. Multi-Attribute Decision Making
MADM is a valuable selection technique currently used in a wide
variety of disciplines, such as management science, industrial engi-
neering, economics, and civil engineering [34]. Although the applica-
tion of MADM in façade engineering is relatively recent, research efforts
have demonstrated the potential benets of this method in enhancing
façade performance by empowering designers to make informed de-
cisions on design parameters based on contextual conditions. Fig. 4
presents a schematic of the overall MADM process. In order to select the
optimal façade among a set of predetermined alternatives, all the
required qualitative and quantitative performance criteria need to be
identied. Several feasible design options are considered and evaluated
by assigning a rating and a weight to each criterion. Subsequently, a
nal score is assigned to the overall performance of each alternative to
identify the best solution. The procedure can account for all the design
attributes and their interdependence, resulting in more accurate evalu-
ations of the performance of each design solution.
MADM methods can be generally classied into: (i) compensatory
methods, producing a single score for each design option, enabling the
identication of the best solution through a process of trading-off or
ranking of the importance of the individual criteria, as for the simple
Weighted Sum Method (WSM), the Analytic Hierarchy Process (AHP)
[35], the Technique for Order of Preference by Similarity to Ideal So-
lution (TOPSIS) [36] and the COmplex PRoportional Assessment (CO-
PRAS) [37]; (ii) non-compensatory methods, making individual
comparisons of all possible pairs of design options through matrices that
show how one option outranks another (pair-wise comparison), without
the need for explicit trading-off, as the ELimination Et Choix Traduisant
la REalit´
e (ELECTRE) [38] and the Preference Ranking Organization
Method for Enrichment Evaluation (PROMETHEE) [39]. This paper does
not present the technical details of the alternative approaches, however,
it discusses the use of MADM in façade design and the main advantages
and disadvantages of each method.
Fig. 3. Façade design process.
S. Bianchi et al.
Building and Environment 250 (2024) 111184
6
Table 1 summarizes the research efforts of various authors who have
worked on validating these MADM procedures in the context of façade
design problems. One of the early applications was conducted by Rey
[40], who applied the ELECTRE method, which is based on partial ag-
gregation and offering a ranking process similar to the thought processes
of decision makers. The author adopted this method to assess the overall
efciency of various retrot façade strategies, considering environ-
mental, sociocultural and economic criteria. Zavsdskas et al. [41] tested
the reliability of alternative approaches, i.e. WSM, Weighted Product
Method (WPM) and Weighted Aggregated Sum Product Assessment
(WASPA) [42], to rank façade solutions of public and commercial
buildings. Rogulj et al. [43] used PROMETHEE method to compare
alternative glass facades by identifying a priority ranking list of alter-
natives which enables the investor to select the appropriate solution.
Akbari et al. [44] examined the functions of a façade in terms of its role
as a connector and barrier for both the interior and exterior of a home.
Using the fuzzy TOPSIS technique [45], they ranked these functions
across different interior spaces of the home and identied that control of
natural ventilation and air ow were the most signicant façade func-
tions for the living room, while providing sufcient daylight was
deemed crucial for the kitchen. Many authors adopted and tested the
application of AHP, most widely applied and well-known technique in
sustainable energy planning of buildings [46–48]. E.g., Moussavi
Nadoushiani et al. [46] applied the AHP method to estimate the relative
weights and rank façade alternatives through a systematic procedure
that accounts for economic and environmental impacts, along with often
overlooked social impacts. The method was employed to identify the
most suitable system to replace an existing old façade. AHP enables
stakeholders to rank decision criteria based on their relative importance
and evaluate alternative solutions through pair-wise comparisons. AHP
assumes that decision problems can be hierarchically structured, with a
one-directional relation between decision levels. By breaking down a
complex decision problem into smaller, more manageable sub-problems,
AHP aids stakeholders in identifying the most critical criteria for a
specic context and making well-informed decisions that strike a bal-
ance between multiple objectives.
Nevertheless, recognizing the distinct strengths and limitations of
individual MADM methods, a hybrid approach that combines different
techniques is gaining popularity [49–51]. This approach aims to
enhance the efciency of the decision-making procedure by leveraging
the advantages of multiple methods. Particularly, the AHP method is
generally used to calculate the weighted criteria, while the decision
variants are ranked through other approaches, such as (i) TOPSIS, where
the alternatives are selected based on their shortest distance from the
ideal solution, which represents the desired outcome, while the perfor-
mance of decision alternatives is measured by comparing their relative
distances to both the ideal and worst solutions; or (ii) COPRAS, which
uses a stepwise ranking and evaluation procedure of the alternatives
based on performance index values, resulting in reduced computational
time. For instance, Ilter et al. [50] applied the AHP in conjunction with
both TOPSIS and COPRAS in the performance evaluation of a glass panel
system, taking into account experimental results to establish the hier-
archical structure. The study revealed that the TOPSIS method yielded
more reliable and consistent results compared to the COPRAS method.
Moreover, different authors sought to develop and apply enhanced
MADM methods. Elkhayat et al. [47] proposed a systematic approach to
rank high performance glazing systems for an existing building by
applying a weighting method based on LEED (Leadership in Energy and
Environmental Design) green building rating system [52]. The weights
of matched criteria in LEED are used as a scale of the relative importance
for the AHP pairwise comparison for establishing the nal weight of
criteria. Chen [53] developed a decision-making tool to facilitate tech-
nological innovation and lifecycle environmental sustainability by using
the Analytic Network Process (ANP) [54], a general theory of relative
measurement and generalization of the AHP method. Unlike other
methods, ANP accounts for relative interdependences among criteria,
however, it is very subjective and time-consuming when a large number
of criteria is involved. Moghatadernejad et al. [55] compared MADM
implementation methodologies and suggested that Choquet integral
[56] is the most appropriate and reliable approach for façade design.
The Choquet method addresses the interdependence among decision
criteria through fuzzy measures, allowing for the consideration of both
quantitative and qualitative measures. When integrated with the AHP
method, it facilitates the generation of consistent preferences, enabling a
comprehensive approach to decision-making. The principal challenge
with the Choquet integral is the identication of the fuzzy measures
when a high number of decision criteria is involved. This can be ach-
ieved either by supervised approaches, if information on the ranking or
the total score of design options is available, or non-supervised ap-
proaches, when judgements from the decision makers are not required.
Moghatadernejad et al. [57,58] presented different methodologies to
derive the fuzzy measures and validate the application of the method in
façade design. The authors highlight that (i) even minor interactions
among criteria can impact the nal ranking of design alternatives; and
(ii) supervised methods offer valuable insights into the relationships
among design criteria, providing valuable guidance when evaluating
new design alternatives. Nevertheless, to establish reliable fuzzy mea-
sures, it is essential to employ a more extensive database. Additionally,
there is a need to develop an easy-to-use framework that simplies the
application of the approach.
The capacity of MADM methods to handle complex decision prob-
lems and offer structured and intuitive decision-making processes makes
them highly suitable for addressing the multifaceted nature of façade
design and selection. However, it is important to acknowledge that the
different MADM procedures have their own strengths and limitations.
Fig. 4. General model of the MADM procedure.
S. Bianchi et al.
Building and Environment 250 (2024) 111184
7
•WSM is the simplest decision-making approach, well-suited for
single-criteria decisions. In design problems with the same unit
ranges across multiple criteria, WSM can be effective and easily
applicable. However, if the different criteria are measured using
different units, the problem becomes difcult to handle [61]. WPM is
similar to WSM with the main difference being a product instead of a
sum. WPM is a dimensionless analysis where each alternative is
compared through a multiplication of ratios related to each criterion.
Therefore, WPM is suitable for both single and multi-dimensional
cases, but it prioritizes the alternatives based on the distance from
the average. WASPA is a combination of both methods and has the
ability to increase the accuracy of ranking. WASPA involves the
optimization of the weighted aggregated function, thereby being
able to reach a higher estimation of accuracy than WPM and WSM
alone [42].
•AHP is the most commonly used MADM method in façade design due
to (i) its use of hierarchies, enabling decomposition of complex
problems into simpler sub-problems, reducing the overall
complexity; (ii) its adaptability without signicantly increasing
computational demands; and (iii) its ability to handle both quanti-
tative and qualitative criteria [55]. Furthermore, the AHP method,
through its mathematical framework and the use of pairwise com-
parisons, provides the most consistent weighting judgments and of-
fers mechanisms to verify data inconsistencies, which signicantly
enhances its reliability in decision-making processes. Additionally,
the method can be easily combined with other MADM methods,
allowing for a more robust decision-making process. However AHP
Table 1
MADM applications in façade design.
Method Authors Aim of the study Decision criteria
WSM Le´
sniak et al. [59] Evaluate variants of façade nishing technologies for one-
storey commercial buildings
5 criteria (cost, frost resistance, maintenance, warranty period,
assembly time)
WSM, WPM,
WASPA
Zavadskas et al. [41] Test the reliability of different methodologies. Select the best
design solution for facades of public and commercial
buildings
12 criteria (installation cost, labour intensity, user friendliness,
durability, warranty, environmental friendliness, recovery,
aesthetics, weight of structure, thickness of structure, sound
isolation, re resistance)
AHP Moussavi
Nadoushiani et al.
[46]
Identify the most sustainable facade system to replace the
existing worn façade of a building
17 criteria (embodied energy and carbon emission, heating load,
cooling load, resource sustainability, material cost, labour cost,
transport cost, maintenance cost, design cost, weight, thermal
resistance, thermal mass, acoustic insulation, resistance to decay,
aesthetics, suitability to location and to climate)
Elkhayat et al. [47] Select the most suitable high-performance glazing system.
Propose a new weighting method based on LEED rating
system to prioritize the alternatives
4 criteria (sustainability, environmental, economic, social), 20
sub-criteria
Dement’eva [48] Compare different hinged ventilated facades used in major
repairs and reconstruction of buildings. Develop a decision
making algorithm to choose the optimal solution
5 criteria (cost, maintainability, life time of service, complexity of
mounting, adaptability)
AHP,
AHP +TOPSIS
Moghtadernejad et al.
[55]
Review of decision-making methods for façade design. Test
the efciency of alternative approaches in a simplied façade
selection
8 criteria (aesthetics, weight, re resistance, acoustics,
environmental impacts, ease of construction, durability, initial
costs)
AHP +TOPSIS, AHP
+COPRAS
Ilter et al. [50] Multi-performance testing on glass panel façade systems. Use
the experimental results in a multi-criteria evaluation process
to determine the performance levels
5 criteria (frontal deection, air inltration, air inltration
difference after seismic test, air inltration difference after wind
test, air inltration difference between rst and last air inltration
test)
AHP +TOPSIS,
Fuzzy AHP +
Modied Fuzzy
TOPSIS
Mukhamet et al. [51] Develop and validate a multi-criteria methodology for
ranking phase change materials for building façade
applications
13 criteria (thermal conductivity, latent heat of fusion, phase
change temperature, specic heat, density, cycling stability,
supercooling, initial cost, toxicity, ammability, corrosiveness,
recyclability, embodied energy)
AHP +PROMETHEE Rogulj et al. [43] Support decisions in selecting the type of and solution for
glass facades of a residential-commercial building
9 criteria (construction cost, maintenance cost, energy budget,
construction complexity, aesthetics, functionality of use, safety,
heat transfer, energy savings)
AHP +
Choquet integral
Moghtadernejad et al.
[57]
Propose and validate a systematic approach to support the
design of optimal façade systems
15 criteria (thickness, weight, re rating, vapour resistance,
thermal resistance, noise reduction, window performance, ease of
construction, energy consumptions, effect on environment,
expected service life, initial cost, operation and maintenance cost,
decommissioning cost, aesthetics)
Fuzzy TOPSIS Akbari et al. [44] Investigate and rank the façade functions in relation to the
inside and outside of a home as a connector and barrier. Find
the importance of façade functions for indoor spaces
7 criteria (visual access, natural ventilation and air ow, daylight,
safety and security, privacy, noise pollution, climate issues)
COPRAS Kaklauskas et al. [37] Develop a method (COPRAS) to select the optimal window
system for the retrot of public buildings
14 criteria (mechanical strength and stiffness, reliability, thermal
transmittance of prole and unit, emission ability, sound
reduction, air permeability, water tightness, warranty period,
durability, light transmittance, duration of works, number of
windows with openings and closing inltration air vent)
ELECTRE Rey [40] Develop a multi-criteria method for ofce retrotting projects 9 criteria (annual energy use for heating, annual electricity use,
annual emissions, summer thermal comfort, acoustic comfort,
visual comfort - natural and articial lighting -, renovation costs,
annual on-going charges)
ANP Chen [53] Develop a multi-criteria model to select the most appropriate
building façade taking into account design, construction and
operation
37 criteria in 6 clusters (adaptability, affordability, durability,
energy, intelligence, well-being)
Yitmen et al. [60] Evaluate the performance of adaptive façade systems in
complex commercial buildings
19 criteria in 5 clusters (energy efciency and environment,
indoor comfort conditions, performance-related functions,
maintenance and life cycle, adaptability)
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Building and Environment 250 (2024) 111184
8
(i) can produce inconsistent ratings when a new alternative is added
to the decision problem at a later stage, as the method is unable to
change the ranking of previously evaluated alternatives [62]; (ii) has
a subjective nature of the modeling process, meaning that the
methodology cannot guarantee denitely true decisions; and (iii)
building the model takes signicantly more time and effort when the
number of the levels in the hierarchy (i.e. the number of pair com-
parisons) increases [63]. Moreover, AHP assumes that there are no
dependencies among the criteria, and this limitation can result in
double-counting in the comparisons. It is worth mentioning that the
ANP method, a more generalized form of the AHP method, can ac-
count for interdependency in the hierarchy by introducing network
relationships. However, ANP is very subjective and scalability issues
when a large number of criteria is involved, due to the sheer volume
of pair-wise comparison combinations required.
•As discussed above, the AHP method is generally applied in a hybrid
fashion: AHP is used to compute the weighted criteria, while the rank
of decision variants is developed through other methods, particularly
TOPSIS and COPRAS. TOPSIS has many advantages, including
simplicity, computational efciency and ability to measure the
relative performance of each alternative using a straightforward
mathematical function that calculates their distance from an ideal
solution. The most signicant disadvantage is the high subjectivity of
the method, which stems from the reliance on subjective judgments
and preferences in the process of assigning weights and determining
the ideal solution [64]. When AHP is used in conjunction with CO-
PRAS, it requires less calculation than when used with TOPSIS. The
AHP-COPRAS combination also enhances the evaluation of both
qualitative and quantitative criteria, due to COPRAS’s ability to
handle multi-criteria problems including those with imprecise or
vague information. The main advantage of COPRAS is that the
method is able to show the degree of utility, representing how well a
given façade design satises the criteria or requirements established
by the decision-maker, by comparing the analyzed façade with the
most efcient one. However, COPRAS is less stable than TOPSIS and
the calculation may be sensitive to the data variation [65]. PROM-
ETHEE is also applied in combination with the AHP model in one of
the studies reviewed. Through this method, the decision maker can
express their preferences between two façade alternatives on all
criteria using ratio scales [66], enabling more accurate evaluation of
each alternative’s performance against each criterion. PROMETHEE
therefore facilitates group-level decision making by allowing the
simultaneous evaluation of multiple alternatives against multiple
criteria. However, the PROMETHEE method is known to be complex
and time-consuming, especially when dealing with large or complex
decision problems.
•Although the aforementioned decision-making methods have proven
useful in selecting the best solution from alternatives, they share a
common limitation: they do not account for the correlation among
criteria. In many decision-making processes, the correlation among
criteria is a critical factor that must be considered to avoid dif-
culties in selecting the best solution when the nal decision scores
are similar. The Choquet integral is an effective method to account
for this interaction and it can be integrated with AHP to improve
consistency in the design selection. The Choquet integral can also
deal with uncertainties associated with decision makers’ judge-
ments. It is worth noting that these uncertainties can be handled by
the other MADM procedures through the integration of fuzzy sets
theory, thereby describing the subjective judgments of decision
makers in a quantitative manner. The Choquet integral has been
applied recently in façade design [55,57]. However, its main
complexity lies in dening the fuzzy measures that describe the
interaction among criteria. This process typically requires inputs
from a panel of experts and can be impractical, especially when
dealing with a large number of criteria. Consequently, the utilization
of the Choquet integral may pose limitations in certain façade
decision-making contexts, making it impractical or challenging to
implement.
3.2. Multi-Objective Decision Making
Multi-Objective Decision Making (MODM) relies on the use of opti-
mization algorithms coupled with numerical simulation to aid designers
in exploring a large number of design options. Building performance
simulation does not generate design solutions, but quanties the per-
formance of design candidates; while, optimization is a method for
nding the best scenario(s) with highest achievable performance under
certain constraints and variables. In general terms, optimization seeks
the minimum or maximum value of an objective function by identifying
the best set of variables within pre-dened constraints. In the context of
façade design, the objective function represents a performance indica-
tor, often computed through simulation. The process consists of (i)
dening design variables and constraints for the specic problem; (ii)
analyzing the performance of these solutions through computational
simulation tools or mathematical equations; (iii) iterating this approach
until convergence to the optimal solution, in terms of a pre-dened set of
performance criteria, is attained. The time and resources required to
complete the process mainly depends on the time needed to evaluate the
alternatives by simulation tools. When conicting goals are involved,
multi-objective optimization algorithms are employed to identify a set of
“non-dominated” solutions or “Pareto frontier” (Fig. 5), meaning that
there is no other feasible solution that improves one objective without
deteriorating at least another one [67].
Optimization tools for façade design consist of programmed algo-
rithms (typically developed in MATLAB or Python), specic optimiza-
tion solvers (such as Octopus in Grasshopper algorithmic modelling) and
general optimization packages (such as GenOpt [69]). Although many
types of optimization techniques exist, performance optimization
frequently employs stochastic population-based algorithms, particularly
Genetic Algorithm (GA) [70]. GAs broadly belong to the gradient-free
optimization family: unlike gradient-based optimization algorithms
that rely on the calculation of gradients or derivatives, GAs do not
require explicit gradient information. GAs draw motivation from pro-
cesses of natural evolution, using mechanisms such as selection, cross-
over and mutation to iteratively explore and search the solution space.
This characteristic grants them scalability and ease of use, particularly
when dealing with discrete variables. The algorithm can efciently
handle non-linear problems with discontinuities and many local
minima. However, they lack the crucial information captured by gra-
dients and Hessians of the objectives and constraints, which affects their
convergence, optimality and stability properties. GA-based multi--
objective optimization methods commonly used in façade research
include Non-dominated Sorting Genetic Algorithm (NSGA-II) [71] and
Strength Pareto Evolutionary Algorithm (SPEA-2) [72]. Empirical per-
formance and evaluations suggest that NSGA-II is able to ensure both the
convergence of the population and its spreading (i.e. to maintain a
diverse set of solutions in the population), while SPEA-2 represents an
improved algorithm which can have advantages over NSGA-II in
multi-dimensional spaces. However, several limitations affect the
GA-based methods, such as the high computational demand due to the
large number of simulations to be run. Moreover, despite their ability to
escape local optima, identifying global ones is often still elusive in
practical large-scale applications. On one hand, micro-GA algorithm
have been developed [73] to allow for a fast converging algorithm with
low computational cost by reducing the population size and number of
generations to converge. On the other hand, it is worth noting that
nding a global optimum may not be necessary, as identifying a set of
alternatives can sufce as an initial design rather than a nal one.
Evolutionary GA-based methods represent the most widely adopted
procedures in façade design, particularly because they handle discrete
variables effectively. Many authors used GA processes in single objective
problems, targeting: (i) natural ventilation, to optimize the shape and
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Building and Environment 250 (2024) 111184
9
position of façade openings [74,75]; (ii) energy efciency, to optimize
the building envelope, and orientation, shape, or both shape and
orientation [76,77]; and (iii) illuminance, in order to optimize shape,
number, position and properties of openings or shading louvers [78–80].
However, to better solve the complex façade design problem and ach-
ieve a high impact on the nal outcomes (e.g. highest performance,
lowest cost, etc.), several studies have focused on multiple objective
GA-based optimization at early stage design. These investigations
involved different performance aspects in the objective functions, such
as: (i) natural ventilation with energy consumption and visual comfort
[81,82], (ii) heating and cooling performance with daylighting [83–86],
(iii) illuminance and glare [78,87]. Due to the importance of both cost
optimality and environmental sustainability in façade design, other
authors embedded either cost effectiveness [88,89] or environmental
impact [90] or both [83,91] within their multi-objective façade opti-
mization. Particularly, Jin and Overend [91] developed a comprehen-
sive methodology based on a whole-life value approach accounting for
social, economic and environmental aspects associated with all the
different stages of the façade life-cycle.
These GA-based investigations traditionally employ a xed geometry
for the building, while variables for optimization usually include phys-
ical properties of materials and construction systems. However, the
building form or geometry is one of the most important decisions at
early-stage design, inuencing aesthetics and building functions whilst
greatly affecting solar radiation receipt, natural light and heat transfer.
Therefore, further studies have focused on building form and facade
optimization in order to minimize the energy consumption while
simultaneously increasing thermal and visual comfort based on solar
radiation or natural light penetration [92–98]. In this context, Jalali
et al. [99] investigated the effect of building envelope changes on the
interior space of the building, rarely taken into account and evaluated on
the basis of the building geometry. Nevertheless, all the cited GA ap-
plications focused on nding one or more solutions for a problem
dened by a limited number of simplied constraints. This contrasts
with the requirements of façade design which typically involves a large
spectrum of design and manufacturing criteria. Taking this into account,
Montali et al. [100] proposed an interactive procedure allowing for a
properly-constrained optimization accounting for both physical features
of the product and the underlying design and manufacturing knowledge
along the lines of a design-for-manufacture-and-assembly approach.
An overview of published research on the use of GA multi-objective
optimization methods in façade design, showing the multiple objectives,
the design variables and, when specied, the type of GA-based method
involved, is shown in Table 2.
Hybrid algorithms, involving the implementation of more than one
optimization algorithms in a hybrid operation, are also widely used in
building design research. The typical procedure consists of (i) adopting a
global search algorithm to nd a near-optimal solution; and (ii) using
the result as a starting point for a local optimizer. A good example of this
operation is implemented in GenOpt [69], a generic optimization
program for the minimization of a cost function that is evaluated by an
external simulation software, such as EnergyPlus or TRNSYS. The hybrid
algorithm in GenOpt consists of a Particle Swarm Optimization (PSO)
[102], another nature inspired computational intelligence method,
which starts searching for a global minimum region, while the
Hooke-Jeeves direct search method [103] continues searching in order
to rene the position of the minimum. Focusing on façade design
problems, the use of hybrid optimization through GenOpt can be found
in various studies involving energy consumptions and daylight or the
life-cycle cost [104,105] as functions to be optimized, and mainly
looking at the design of curtain wall facades for new ofce buildings
[106,107]. Within the nature-inspired algorithms, Chatzikonstantinou
et al. [108] tested the applicability of Differential Evolution (DE) [109]
to design a diagrid façade considering interior daylight distribution and
panel construction cost in the objective functions, showing that DE can
be another effective tool for façade design problems.
Concluding, MODM can be used to extensively explore the full realm
of design possibilities and generate optimal solutions. By coupling
building performance simulation and optimization, and accounting for
multiple performance objectives and project-related constraints, high-
performance solutions can be identied at the early stages of the
design process.
•Initial MODM applications in façade design focused on single-
objective problems. However, single-objective optimization is
driven by dominating contributions, which can be controlled by
weighting averages over individual contributions [110]. Therefore,
this approach suffers from various drawbacks, such as the a-priori
selection of weights and the convergence to a single solution which is
affected by this weight selection.
•Evolutionary algorithms are the most popular methods for solving
multi-objective façade optimization. Most of the authors employed
GA, an efcient family of algorithms for searching in a guided
manner the state space to nd near-optimal solutions. GA is a sto-
chastic algorithm that can handle both discrete and continuous
problems. However, the method is computationally expensive due to
the large amount of simulations to be run to ensure optimality of the
nal solution, and it typically lacks convergence guarantees. To
address this issue, some researchers have employed the micro-GA
algorithm, which utilizes small populations that are more efcient
at identifying promising areas of the search space [111]. Although
small populations may face challenges in preserving diversity over
multiple generations, it is possible to retain the best-t individuals
and restart the population when diversity is lost.
•Within the evolutionary algorithms, the DE method has also been
tested in the context of façade design. DE is a population-based
stochastic algorithm with multiple advantages, such as simplicity
due to few control parameters, local searching properties, and fast
convergence. Moreover, it has been demonstrated that DE is often
able to explore the decision space more efciently than GA [112].
Fig. 5. Pareto frontier (modied after L´
opez Jaimes et al. [68]).
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Building and Environment 250 (2024) 111184
10
Table 2
GA-based multi-objective applications in façade design.
Method Authors Aim of the study Objectives Variables
Micro-
GA
Caldas [83] Develop an evolutionary-based generative design
method to achieve energy-efcient and sustainable
solutions
Case 1: annual illumination
Case 2: natural lighting and
energy performance
Case 3: energy consumptions,
construction costs, embodied
energy
Case 4: daylight use and thermal
performance
Case 5: energy efciency,
Case 6: heating energy and
daylight
Case 1: geometrical description of spaces, facades,
roofs and other elements,
Case 2: alternative façade solutions,
Case 3: building materials
Case 4: as Case 1
Case 5: architectural form,
Case 6: shape generation
Gagne and
Andersen [78]
Explore facade design based on illuminance and/or
glare objectives
Illuminance,
Daylighting glare probability
10 variables: Window-to-Wall ratio, number of
windows, aspect ratio, vertical and horizontal
location, window distribution, overhang, ns, length
of shading devices, total glass transmissivity, percent
transmission
GA Torres and
Sakamoto [81]
Determine the applicability of a GA for the
optimization of daylighting systems
Visual discomfort and daylight
penetration
21 variables encoding size, number, position of
windows and xed protections, reectance of surfaces
Shan [88] Provide a method for optimizing building facade to
achieve the minimum annual energy cost
Heating, cooling and lighting
loads, total cost
Dimensions of window grids, depth of the shading
system
Marzban et al.
[82]
Optimize single-sided naturally ventilated
residential buildings
Ventilation efciency, cooling/
heating load and number of
discomfort hours, and visual
comfort
13 variables related to openings geometry and types,
balconies geometry, type of shading, construction and
insulation types, neighboring units
Montali et al.
[100]
Develop and validate a process to build product-
oriented knowledge bases and design tools and help
designers nd optimal façade solutions
Operational and embodied carbon Panel height and width, position of panels, air layer
thickness, window position and height, concrete inll
position and height
NSGA-
II
Evins et al.
[101]
Derive the best conguration and control of Double-
Skin Facades
Cooling and heating load 20 variables related to cooling and heating mode,
control glazing
Chantrelle et al.
[90]
Develop and validate a multi-criteria tool for the
optimization of renovation operations
Cooling, heating, lighting,
ventilation, thermal discomfort,
environmental impact
Variables related to control strategies and HVAC
systems; 6 variables for the building envelope:
external wall type, roof type, ground oor type,
intermediate oor type, partition wall type, window
type
Kasinalis et al.
[84]
Develop a framework for design and performance
analysis of climate adaptive building shells with
optimal seasonal adaptation strategies
Heating, articial lighting, indoor
environmental quality
Density, specic heat, thermal conductivity, external
surface absorbance, window-to-wall ratio, glazing ID
Jin and Overend
[91]
Develop a prototype whole-life value optimization
tool for façade design; test the tool on a real-world
façade design project
Whole-life cost,
Whole-life carbon emissions
8 variables related to: glazed facade geometric
parameters, glazing types, spandrel panel type
M´
endez
Echenaguci
et al. [85]
Develop an integrative approach to obtain detailed
information on energy efcient envelope
congurations
Heating, cooling and lighting
energy
Thickness of the masonry wall; number, shape and
placement of windows; glazing characteristics of the
windows
Yang et al. [89] Apply multi-objective optimization to design green
building envelope material
Envelope construction cost and
energy performance, window
opening rate
Number of windows, window length, window width,
window glass material, wall material, glass curtain
material, roof material, sunshade type, sunshade
board size
Li et al. [80] Develop an approach combining machine learning
and computer-aided design methods for adaptive
facades
Daylight illuminance, daylight
autonomy, daylight glare
probability
Unit type, unit width, unit height, unit distance,
rotation angle, room width, room length, room height
Wen et al. [87] Develop a parameterization method for the selection
and design of a shading strategy
Spatial glare autonomy, spatial
daylight vote autonomy
Hole diameters, rotation angle, slat width, slat
number, extension length
Nazari et al.
[98]
Optimize a commercial (retail stores and
supermarkets) building envelope
Heating and cooling energy,
Indoor environmental quality
Orientation, diameter ratio (ratio of the building large
diameter to its small diameter), window-to-wall ratio
SPEA-2 Jin and Joeng
[93]
Dene an optimization process for a free-form
building shape in the early design stage
Envelope heat gain, heat loss and
solar heat gain
Shape and footprint of the building
Moraes and
Pereira [94]
Develop a process to optimize building facades for
solar irradiation
Economic viability of the
photovoltaic installation
4 variables related to building shape
Zhang et al.
[95]
Optimize the thermal and daylight performance of
school buildings
Energy use for heating and
lighting
Orientation, room depth and corridor depth, window-
to-wall ratio of different interfaces, glazing materials
and shading types
Fathy and
Fareed [96]
Optimize the design of a parametric double skin
façade to maintain sufcient daylighting conditions
to meet LEED requirements while maximizing
energy savings
Illuminance intensity, energy
loads from cooling and heating
Distance between the outer skin and the inner skin,
depth of the outer skin, openness factor in the outer
façade, scale factor of the opened cells
Fang and Cho
[97]
Develop and test an optimization process to evaluate
the daylighting and energy performance of design
options and generate optimized design
Daylight illuminance, energy use Building depth, roof ridge location, skylight width,
skylight length, skylight location, south window
width, louver length, north window width, and
skylight orientation
Jalali et al. [99] Optimize an ofce building facade through a genetic
algorithm with the sustainability approach
Thermal load; useful space inside
the building; shape coefcient;
amount of natural light
Changes in angles, form rotation, lengths and widths,
building heights, and number of oors
Kim and
Clayton [86]
Develop a multi-objective optimization framework
to support the climate-adaptive building envelope
Cooling load, daylighting
performance in summer season
Operation scenarios in the climate adaptive building
envelope system
(continued on next page)
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Building and Environment 250 (2024) 111184
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However, the efciency of the search for the global minimum is very
sensitive to the choice of the control parameters and can be disad-
vantageous in some global optimization problems [113].
•Several authors adopted existing optimization software packages in
their multi-objective studies, such as Octopus plug-in for Grass-
hopper environment and GenOpt [69], a general optimization pro-
gram which can be easily coupled with any simulation software.
Octopus is based on SPEA-2 algorithm, a GA-based method able to
keep the desired convergence properties and to maintain a good
distribution of solutions. GenOpt involves a hybrid algorithm
combining PSO with direct search methods. PSO is known to have
difculty in nding the global optimum in high-dimensional spaces
and can have a low convergence rate in the iterative process [114].
Direct search methods, on the other hand, can improve solutions as
long as they overcome small discontinuities in the cost function and
small local optima. By combining both methods, GenOpt aims to
achieve better performance in terms of nding the global optimum
while overcoming the limitations of each individual method. These
existing tools provide ease of access to optimization processes and
ease of application for the majority of designers who are not experts
in computational optimization.
•In multi-objective optimization for façade design, it is important to
consider constraints related to manufacturability in addition to the
design optimization problem. However, many research studies tend
to focus solely on the optimization problem without taking into ac-
count manufacturability constraints. To address this issue, advanced
performance modeling methods have been developed that incorpo-
rate both design and manufacturing knowledge, resulting in a more
reliable and practical optimization-based design process. These new
design processes involve iterative optimization procedures that are
constrained by the physical features of the product, allowing for the
identication of solutions that are both optimal and manufacturable
[100].
3.3. Role of machine learning methods
Building/façade optimization typically involves a large-number of
computationally intensive simulations. For instance, for an energy ef-
ciency assessment, multiple calls may be required to cover energy per-
formance over a meteorological year, whereas, in a seismic
vulnerability/fragility assessment, a large number of nonlinear dynamic
analyses is necessary to adequately quantify safety-related risks. A way
to address this challenge is by combining machine learning methods
with multi-objective optimization techniques. Surrogate models built on
simulation data, such as Articial Neural Networks (ANNs) [115], are
often used to expedite the process. ANNs, inspired by the biological
nervous system, map inputs to outputs through layers of connected
neurons. Training ANNs involves adjusting weights by backpropagating
errors until predicted outputs match pre-specied targets. Once properly
trained and validated, ANNs act as surrogate models, substituting
external simulation programs. Coupled with multi-objective algorithms,
they can assess objective and constraint functions faster within accept-
able accuracy losses. However, quality outcomes depend on the data
used during the learning phase, typically requiring substantially large
datasets of carefully selected features for effective training through
numerous ofine simulations. Finding optimal ANN structures and
hyperparameters also involves a signicant component of
trial-and-error by the user, in order to strike a balance between
computational demand and accuracy. Proper training, validation, and
testing are crucial for ANNs generalization to new unseen data. This is
particularly important when coupling surrogates, such as ANNs, with
optimization methods, since optimal designs may lie outside the limited
subspace captured by the training data.
The implementation of ANNs coupled with optimization in façade
problems has mainly emerged in recent years. Particularly, the ef-
ciency and advantage of applying ANN-based performance simulation
with GA has been studied in different problems involving annual energy
load and summer thermal comfort [116], cooling energy with thermal
comfort and indoor air quality [117], thermal and visual comfort with
both energy consumptions and energy costs, daylight and energy [87,
118,119], energy and cost objectives [121]. Physics-informed neural
networks are also emerging in building design (e.g. [120]) as they
combine data-driven approaches with the mathematical properties
inherent in physics-based problems. Consequently, these networks
typically require less data while still capturing the underlying physics
accurately. The above-described ANN implementations, either under
their purely data-driven or physics-informed instances, belong to the
general family of supervised learning approaches.
Reinforcement learning [122] is another machine learning family
that is gaining attraction in the context of sequential decision-making
and control optimization for facades/buildings. Deep Reinforcement
Learning (DRL) is an extended version combining classical reinforce-
ment learning principles with ANN parametrizations. This paradigm
empowers a computer program (agent) to learn decision-making stra-
tegies that achieve specic goals. In traditional reinforcement learning,
the agent learns by interacting with its environment and taking actions
to earn rewards. DRL enhances this process since neutral networks allow
us to handle more complex state and decision spaces. Training DRL
agents can be, however, often computationally intensive due to the need
for numerous simulations and interactions with the environment. An
example of DRL applications in façade/building optimization can be
found in the work of Han et al. [123], where the technique is used to
optimize the timing of window opening/closing by observing and
learning from the environment. Another application can be found in
Park et al. [124], who utilize DRL to learn individual occupant behaviors
and indoor environmental conditions, adapting control parameters
accordingly through personalized set-points.
Furthermore, initial research is being further conducted on Multi-
Agent Systems (MAS) approaches within the reinforcement learning
agent-based paradigm. Multi-agent-based design allows modeling
different domains as agents to support design exploration through
heuristic search. It facilitates adjusting parameters and identifying
façade alternatives based on preferences and performance goals. How-
ever, MAS presents challenges in emergent behavior, system robustness,
and reliability [125]. Emergent behavior arises when the system’s
overall behavior is not a simple sum of individual agents’ actions,
leading to hard-to-control and unexpected outcomes. System robustness
deals with the ability to function despite agent or component failures.
System reliability, on the other hand, refers to the system’s correct and
consistent performance over time. Initial research towards the
Table 2 (continued )
Method Authors Aim of the study Objectives Variables
design decision-making process using a parametric
behavior map
Fan et al. [98] Develop a multi-objective facade optimization
method for stadium design, using image density
atlas
Daylight illuminance, solar
radiation load and daylight glare
probability
Opening and closing degree of the gymnasium facade
S. Bianchi et al.
Building and Environment 250 (2024) 111184
12
integration of performance-based goals with geometric formation
through a decentralized and agent-based approach can be found in the
research developed by Gerber et al. [126] and Pantazis and Gerber
[127]. The authors developed a MAS-based design tool for daylighting
design and optimization. This approach allows for customization to
align with the specic needs of designers. The software works through
generative agents and behaviors initially acting alone to develop design
alternatives. The alternatives are subsequently analyzed by a set of
specialist and user preference agents, that communicate their data back
to the generative agents to adjust parameters and regenerate design
alternatives based on specic preferences and performance goals.
Apart from reinforcement learning, generative articial methods are
also emerging for façade and building mass generation. Deep generative
design, within the unsupervised learning family, leverages deep learning
techniques like neural networks to autonomously generate and optimize
designs, effectively automating and enhancing the design process
through the integration of generative algorithms. However, deep
generative design often requires a large amount of design data, which
may not always be accessible, and the resulting designs may lack clear
explanations, making it challenging to understand and validate the
design process. Within these methods, various authors have imple-
mented Generative Adversarial Networks (GANs) [128] in the context of
façade applications. In the context of urban renovation, Yu et al. [129]
applied GAN technology in the context of urban renovation for façade
recognition and generation, showcasing its potential for façade review,
digital design, drawing assistance and for expanding creative work using
AI. Sun et al. [130] developed a GAN-based decision-support tool to
automatically generate stylized facades, evaluated its effectiveness
through quantitative and qualitative assessments, and demonstrated its
signicant potential for enhancing the conventional design process for
historic urban area renovation.
3.4. Conclusions from the reviewed studies
Based on the collected publications, the literature data are further
analyzed to identify the dominant performance criteria and the types of
applications that have been explored in these research studies (Fig. 6).
Energy efciency and daylight control (in 33 and 35 studies, respec-
tively) are the predominant aspects identied as important criteria or
objective functions in MADM or MODM. Specically, optimization
mainly focuses on minimizing the cooling energy and the total energy
consumptions and maximizing visual comfort for occupants. Several
studies also account for the initial cost and thermal comfort (each in 18
studies) as primary performance aspects for façade design, with the
thermal comfort typically expressed in terms of Predicted Percentage of
people Dissatised. Moreover, both structural performance, i.e. the
verication of allowable stresses and/or displacements, and the envi-
ronmental footprint, particularly in terms of embodied energy, are
involved in many works (16 studies). Focusing on the functional cate-
gory, different MCDM works also aimed at glare control (13 studies), air
quality (13 studies) and aural comfort (11 studies), to target an opti-
mized comfort for building occupants. Particularly, these studies
focused on satisfying Indoor Environmental Quality within their MCDM
procedures. Durability is also involved in different MADM procedures by
evaluating the expected service life of the façade system (8 studies),
while few researchers accounted for water penetration resistance (4
studies) and condensation resistance (2 studies) as additional durability
criteria. Regarding the cost-effectiveness, rehabilitation and mainte-
nance cost are also involved (11 studies) in MADM approaches, while
operation cost (8 studies) and disassembly cost (5 studies) are consid-
ered when full life-cycle analysis are conducted. It is worth noticing that
re protection is also investigated in some MADM investigations (6
studies), while both blast resistance and dynamic performance are ab-
sent in the reviewed papers. Finally, due to the increasingly strong
sensitivity to the environment, recent MADM investigations also account
for reuse and recycling (3 studies), use of renewable resources (2
studies) and climate adaptability (3 studies) to select the best façade
amongst alternatives. The data also indicate that the majority of papers
(73 %) utilize MCDM procedures for new designs, with a particular
emphasis on selecting the optimal façade for ofce buildings (42 %). The
remaining studies reviewed (27 %) focus on façade retrotting/refur-
bishment, primarily examining public structures such as school build-
ings (29 %).
Referring to the implemented methodologies, Fig. 7 provides an in-
clusive overview of all the MCDM approaches (where main categories
are depicted by rectangular shapes, while dashed lines indicate the
integration of techniques) adopted in the reviewed papers discussed
earlier. MCDM offers a structured approach to evaluate and rank alter-
natives based on predened criteria, aiding informed choices by
considering design attributes’ relative importance. In contrast, MODM
provides a comprehensive approach, exploring the full range of possi-
bilities by considering multiple conicting objectives simultaneously.
MODM facilitates the search for Pareto-optimal solutions that balance
competing design objectives through trade-offs. The choice between
MADM and MODM depends on specic requirements and the solution
space each method can explore, taking into account design space
complexity and desired levels of exploration and optimization. MADM is
effective for scenarios with a predened set of facade solutions, enabling
decision-makers to prioritize selection based on predened criteria.
MODM is more appropriate when there is a need to optimize the design
space. Combining MADM and MODM provides a comprehensive
decision-making framework, encompassing both predened options and
design space exploration. If the MADM space is smaller than the MODM
space, a designer can start with an MADM solution for a quicker iden-
tication and then initiate MODM optimization based on that initial
‘guess,’ or vice versa.
Despite the exploration and comparison of various MCDM methods
in the literature, further investigation is needed to determine the most
suitable method for façade design. Stakeholders’ requirements greatly
inuence the selection of an optimal façade design, and the choice of
optimality criteria can yield different outcomes with different methods.
With regard to MADM methods, there is still a need to identify a reliable
and user-friendly design support tool that can effectively combine
quantitative and qualitative data while accounting for attribute corre-
lation. This will facilitate a shift from traditional façade selection pro-
cesses to performance-based decision-making processes that embed
design priorities for the specic case-study scenario. In MODM methods,
dening a real design problem in a mathematical domain has some
limitations. However, by using MODM tools and algorithms in combi-
nation with their expert knowledge, designers can improve façade per-
formance compared to common practices that do not involve
optimization. Designers can reduce the solution space or steer the search
in the right direction by using MODM tools and algorithms. Moreover,
the integration of machine learning techniques can further improve the
efciency and effectiveness of the design optimization, opening up new
possibilities for creative and high-performance façades (see Section 3.3).
As such, research is ongoing to integrate optimization into real-world
façade design processes effectively. This involves identifying suitable
approaches tailored to specic façade problems, considering factors like
design complexity and optimization objectives. Furthermore, there is a
need to explore and develop techniques for reducing simulation time
when optimizing alongside building energy simulations.
4. Opportunities and research challenges
Recent developments and drivers largely from outside the eld of
façade engineering present notable opportunities and associated
research challenges for multi-criteria methods in façade design. These
are identied and discussed in turn in this section.
S. Bianchi et al.
Building and Environment 250 (2024) 111184
13
4.1. Uncertainties and risk analysis
The robustness of a design decision is unknown unless uncertainty is
considered explicitly in the design process. In this way the performance-
based design solution not only fullls the pre-established performance
requirements, but also provides acceptable performance under uncer-
tainty. However, multi-criteria façade design typically neglects the in-
uence of uncertainty (modelling and hazard-related) as well as the risk
attitude of stakeholders in decision making.
In the literature, there are initial studies that deal with uncertainty in
MADM methods. Hopfe et al. [62] highlighted the effectiveness of
including uncertainty in a AHP process in order to inform the design
team about the predicted building performance and its risks, thus
addressing a rational decision. Muin et al. [131] developed a decision
making framework involving uncertainty quantication (for light,
thermal energy consumptions and initial costs) and probabilistic anal-
ysis to assess the performance of conventional vs. innovative facades.
Homaei and Hamdy [132] proposed a new decision making method, the
“T-approach”, integrating robustness assessment and dening a
multi-target key performance indicator based on the building perfor-
mance and deviations from performance targets. Moschetti et al. [133]
validated an extension of this approach to identify the most robust
responsive building envelope.
Focusing on optimization problems, uncertainty in design variables,
environmental parameters, and noise in the output and constraints
should be considered to develop a robust multi-criteria optimization
[134], which leads to the identication of a global robust Pareto front.
Despite not focusing on façade only, an initial application of a robust
optimization can be found in the recent work developed by Chang et al.
[135], who proposed a GA-based multi-objective algorithm accounting
for uncertainties in the objective functions. The authors aimed to iden-
tify the optimal building envelope renovation options to satisfy indoor
thermal comfort, energy balance, environmental emissions and eco-
nomic aspects. However, embedding all the different sources of uncer-
tainty would lead to a highly computationally intensive process,
therefore, sensitivity analysis should be carried out to identify the most
sensitive design variables. Furthermore, to reduce simulation time and
assess robustness more quickly, meta-models or surrogates can be uti-
lized to approximate the objective function. For instance, Hopfe et al.
[136] employed the highly accurate Kriging metamodel for the design
optimization of an ofce building, while Li et al. [137] utilized ANNs as
the building performance model in their robust design optimization of
entire zero/low energy buildings, which included both the building
envelope and services.
Selecting a robust high-performance solution is challenging, and
inaccurate descriptions of uncertainty in design parameters can result in
improper uncertainty analysis. However, a risk-based MCDM procedure
that involves performance evaluation and uncertainty assessment could
be effective to address investment decisions in façade design. Uncer-
tainty quantication should encompass both epistemic uncertainties
associated with the specic design problem, such as mechanical prop-
erties, loading/mass values, design variations (e.g., equipment density,
load people, heating and cooling set points, etc.) and aleatoric un-
certainties associated with the specic hazard, such as earthquakes or
hurricanes [138,139]. Accounting for all these uncertainties is essential
for developing a robust assessment framework, potentially enabling the
denition of uncertainty factors for use in simpler and more practical
analyses. This can result in solutions with better performance and
reduced cost when compared to (semi-) deterministic design [140]. It is
therefore essential to explore uncertainty-based MCDM methods, strik-
ing a balance between computational time and analysis complexity, to
enable decision-makers to robustly evaluate design alternatives.
4.2. Seismic safety considerations
Although simplied or advanced multi-criteria design approaches
have been proposed, none includes the seismic safety within the per-
formance indicators. Seismic performance is hardly mentioned when
dening the general design framework, while no practical application
involving seismic safety of building facades can be found in the litera-
ture, when the majority of the buildings investigated in the reviewed
papers are located in seismic-prone areas (Fig. 8a).
Facades may lose functionality even in low-intensity earthquakes,
and can be seriously damaged or destroyed in moderate-to-high in-
tensity events (Fig. 8b). This poses a potential life-safety threat to
building occupants and pedestrians, while leading to substantial socio-
economic losses (even greater than the structural losses), negative
impact to the environment, and market disruption. Moreover, building
facades represent a large portion of the construction investment in
buildings, e.g. unitized systems can account for 20–30 % of the total cost
and this percentage could even increase when more functions such as the
active contribution to the building services is considered [3].
As such, the development of innovative damage-control or
earthquake-proof technologies for facades is crucial for enhancing
community resilience in seismic hazard zones [143,144]. However, it is
equally important to incorporate the seismic performance of facades
Fig. 6. Performance criteria and type of application investigated in the reviewed papers.
S. Bianchi et al.
Building and Environment 250 (2024) 111184
14
into the decision-making process from the early stages of design. By
doing so, stakeholders can select high-performance and safer solutions,
thereby preventing severe economic losses and business interruption
due to severe damage after earthquakes. Initial research efforts are
acknowledged towards the development of integrated approaches
combining seismic safety, environmental sustainability and cost effec-
tiveness for retrot/refurbishment interventions [e.g. [145,146]].
However, these investigations have mainly focused on simplied pro-
cedures and global building-level assessments. Moreover, a consolidated
framework for façade design and retrotting that integrates seismic
safety with other performance measures, based on MADM or MODM or a
combination of both, is still lacking. A design procedure and tool should
be developed to support the selection of resilient façade technologies
and interventions in earthquake-prone regions. Furthermore, while this
paper specically focuses on seismic safety, the integration of safety
considerations is applicable to other natural disasters or extreme events,
such as hurricanes or oods, based on the specic multi-hazard local
scenario.
4.3. Smart building information modeling
BIM is a powerful tool that facilitates cross-disciplinary collaboration
and knowledge sharing among stakeholders involved in the design,
construction and operation of a building. BIM is a multi-dimensional
model that improves work efciency, enhances quality, and minimizes
the risk of data loss. Applying BIM from the early design phase can
provide positive impact to a project, being cheaper and better to make
changes earlier than in a later design phase [147]. Additionally, inte-
grating BIM and MCDM procedures certainly represents another
important step forward to enhance façade design and facilitate the
application of decision-making methods to designers, enabling stake-
holders to collaborate across their value chains. Efforts to integrate BIM
and façade optimization can be seen in the initial research conducted by
Gagne and Andersen [78]. The authors integrated GA methods with BIM
to allow designers to customize both the design and the performance
goals through building models, automatically generated during the GA
process. Recent investigations [e.g.148-150] have focused on the
development and validation of BIM-based frameworks to enable de-
signers to explore design options through a visual programming user
interface (e.g., Dynamo in Autodesk Revit), by generating models of the
alternative solutions, assessing the performance of the models and
searching for the optimal design. All of these studies demonstrate that
using BIM in the decision-making process provides several benets,
including: (i) increased condence in the generated solution by
balancing construction and production constraints with design re-
quirements, (ii) visualization of the effect of changes in the model and
planning process for designers and clients, and (iii) enhanced level of
automation. Additionally, BIM-based simulations can account for other
dimensions such as schedule management, allowing for better control of
overall façade performance from a life-cycle perspective. As more in-
formation is integrated into the BIM model, a more holistic process and
optimization can be achieved.
Looking at automatization procedures within the Industry 4.0 tech-
nologies, integrating MCDM with Augmented Reality (AR) presents an
exciting opportunity in façade design. This integration could provide a
simpler and more accessible way for building users, customers and
suppliers to interact with the design process. One of the rst applications
and proofs of the advantages of this integrated method can be found in
the work developed by Sangiorgio et al. [151]. They proposed an
AR-based decision-making procedure based on the hierarchical struc-
ture of AHP. The authors validated the method for precast concrete
panels accounting for alternative criteria such as aesthetics, production
and executive needs, thermal behavior, and costs. The AR-based
approach was found to be fast and intuitive, allowing the
decision-maker to carry out a comparison of the design parameters
through a simple procedure. Specic models helped in understanding
the problem parameters, and consistency tests were conducted accord-
ing to the AHP theory. Therefore, the main advantage of an AR-based
approach is its ability to communicate and involve non-expert deci-
sion makers. However, this approach may only be effective when certain
performance criteria are involved, while other criteria may require more
advanced simulations and procedures to identify the best solution.
Consequently, more studies are required to investigate the effectiveness
of this approach for a wider range of criteria and design scenarios, and
investigate its potential in real-life design and construction projects.
4.4. Climate-resilient façade design
The built environment is increasingly facing events due to climate
change, such as high temperatures, strong winds and heavy rainfall.
Buildings have not been designed for these climate-related extremes and
they are vulnerable and non-resilient to such events (insured losses for
climatological events alone are 7–16 % of the total economic losses in
Europe [152]). As a result, building systems often struggle to fulll their
primary functions, such as ensuring structural safety, as well as opera-
tional functions like providing comfortable environments for occupants.
Fig. 7. MCDM façade engineering methods applied in the reviewed papers.
S. Bianchi et al.
Building and Environment 250 (2024) 111184
15
These climate-related events add another series of hazards to the other
disruptive events such as earthquakes or explosions. Altogether these
hazards have signicant consequences on the resilience of the overall
building and, in particular, of the building facades. Building a resilient
society is therefore increasingly recognized as a socio-economic and
political priority and the overarching goal of risk reduction and man-
agement policies. Within the building industry, this means that building
components should be able to absorb and recover from the effects of an
external event as well as to adapt to the changing environment. The
concept of resilience is dened differently depending on the research
eld, however it is typically expressed as the capacity of a system to
“bounce back” to an equilibrium state (or “bounce forward”) within a
recovery time, after absorbing the impact of an external, natural or
man-made, event [153]. Resilience refers to both strength and exibility
of a building, and facades certainly play a critical role to reduce the risk
of sub-standard performance levels.
Although research is increasingly moving towards the study of
climate change impacts on building facades (e.g. [154,155]), research
efforts are needed to develop effective frameworks and tools for
assessing and mitigating the overall multi-hazard risk of building fa-
cades. Such frameworks must account for the short- and long-term
consequences associated with weather variations and extreme events.
Furthermore, to support the design of resilient facades, resilience con-
siderations should be included in multi-criteria decision making from
the early stage design, where key decisions are made to target
high-performance designs. This would help stakeholders to identify
appropriate designs or mitigation measures based on their positive
impact in terms of resilience. However, in order to be included in the
design process, a quantiable resilience index/measure should be rst
identied. While there are emerging concepts, denitions and initial
frameworks for assessing thermal resilience in the literature [156–158],
dening facade resilience requires an indicator or curve that accounts
for all potential events that may occur over the lifespan of the facade.
This includes the impacts of both gradual environmental changes and
disruptive events (Fig. 9). The long-term effect of climate change has
impact on the façade Robustness, its inherent strength to withstand
external demands without degradation or loss of functionality, and
Redundancy, dening the inclusion of redundant elements or properties
to provide backup or alternate options when subjected to stresses or
failures. In case of an extreme event, Resourcefulness, meaning the ca-
pacity to manage operation and/or resources and services under emer-
gency conditions, and Rapidity, at which disruption can be overcome,
are also mobilized. These represent the four principles of resilience or
the so-called 4R’s following the framework dened by Bruneau et al.
[159]. Concluding, an integrated resilience indicator should be identi-
ed and embedded as design parameter and/or target objective in the
early stage design of building facades to dene robust, adaptive and less
vulnerable solutions.
5. Conclusions
Developing effective design methods is crucial for achieving high-
performance facades. Today, designers have access to a wide range of
computation-based approaches and techniques, enabling automation
and enhancement of the design process, especially at the early stages.
Facade design is intricate, involving numerous variables and conicting
performance criteria that require complex decisions. Complexity is
amplied by project goals, limitations, and constraints specic to each
case study. Optimal facade solutions should not merely meet re-
quirements but aim to maximize the overall performance and consider
interactions among various criteria.
To develop an integrated approach for façade design, research efforts
to-date have largely focused on the identication of key performance
criteria, and the denition and validation of decision-making methods to
support the façade selection. Referring to several publications on this
topic, the paper has provided an overview of the main methodologies
currently employed in façade design. The description has focused on
Multi-Criteria Decision Making (MCDM) procedures, including both
Multi-Attribute Decision Making (MADM) and Multi-Objective Decision
Making (MODM). The paper has also explored the integration of ma-
chine learning within MCDM, which is increasingly recognized as a
method to streamline the design process and empower architects to
make informed data-driven decisions. The investigation has revealed
that many MCDM procedures tend to address energy efciency and
daylight control. MADM is a valuable technique to support the choice of
the best solution from a set of predetermined alternatives. Despite being
affected by the subjective preferences of decision makers, there are some
promising trends in MADM. First, hybrid approaches combining
different methods are becoming increasingly popular. Second, there is a
growing interest in methods that account for the correlation among at-
tributes, such as the Choquet integral, and for uncertainties in the design
criteria, often by using fuzzy set theory. However, MODM offers a more
rigorous framework at early stage design, enabling to explore the full
realm of design possibilities and target optimal solution(s). In façade
design MODM mainly considers nature-inspired algorithms, often
combined with metamodels that mimic the behavior of external simu-
lation programs to reduce the computational time. Notwithstanding the
tendency to focus primarily on the mathematical optimization problem
and the difculty of achieving a real optimal solution, research efforts
are further aiming at enhanced design procedures that account for
manufacturer knowledge or consider the interaction between the
different expertise involved. However, further investigations are needed
to establish design criteria and objective functions based on the case-
study scenario (i.e. type of application and building use) and identify
the most appropriate MADM and MODM (or their combination)
approach to be implemented for a specic façade problem. This will
provide a useful guideline to designers, thus facilitating the integration
of these design approaches in the common practice.
Fig. 8. (a) Map indicating the seismic zones (light-to-dark grey areas, representing low-to-very high seismicity) and locations involved in the reviewed MCDM
studies; (b) Damage to building facades [141,142].
S. Bianchi et al.
Building and Environment 250 (2024) 111184
16
To capture latest developments, the paper has also discussed on-
going trends that are likely to affect the future of façade design.
Particularly, robust multi-criteria design processes accounting for
different sources of uncertainties are needed to increase effectiveness
and provide the reliability/risks associated with a specic façade.
Although the majority of the buildings investigated in literature are
located in seismic-prone countries, none of the applications involve
seismic safety as decisive criterion. However, seismic considerations
should be integrated to create safer systems and reduce the negative
socio-economic-environmental impact of earthquakes. To further
enhance the design, automation should be used to increase quality and
facilitate the application of multi-criteria methods and the interaction
with stakeholders. Furthermore, due to the vulnerability of facades to
climate-induced extremes, the urgent need for resilient solutions is being
increasingly recognized. The principal challenge here is to quantify
resilience and integrate resilience considerations into the design pro-
cess. This includes incorporating resilience as a key objective in multi-
objective design methods, to ensure that the resulting facades are not
only resource-efcient and user-centered, but also capable of with-
standing a range of environmental stresses and hazards.
CRediT authorship contribution statement
Simona Bianchi: Writing – original draft, Visualization, Investiga-
tion, Funding acquisition, Conceptualization. Charalampos Andriotis:
Writing – review & editing. Tillman Klein: Writing – review & editing.
Mauro Overend: Writing – review & editing, Conceptualization.
Declaration of competing interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Data availability
No data was used for the research described in the article.
Acknowledgements
This study has received funding from the European Union’s Horizon
2020 research and innovation program under the Marie Skłodowska-
Curie grant agreement No. 101029605 (H2020-MSCA–IF–2020 - SAFE-
FACE - Seismic SAFety and Energy efciency: Integrated technologies
and multi-criteria performance-based design for building FACadEs) for
Dr. Bianchi. Dr. Andriotis would further acknowledge the support by the
TU Delft AI Labs program.
References
[1] European Parliament and the Council of the European Union, Directive
Amending the Energy Performance of Buildings Directive, 2018/844/EU..
[2] European Parliament and the Council of the European Union, Energy Efciency
Directive, 2023/1791/EU..
[3] T. Klein, Integral Facade Construction - towards a New Product Architecture for
Curtain Walls, Architecture and the Built environment, Delft, 2013.
[4] J. Montali, M. Overend, P.M. Pelken, M. Sauchelli, Knowledge-based engineering
in the design for manufacture of prefabricated facades: current gaps and future
trends, Architect. Eng. Des. Manag. 14 (2017) 1–17, https://doi.org/10.1080/
17452007.2017.1364216.
[5] Q. Jin, M. Overend, Sensitivity of facade performance on early-stage design
variables, Energy Build. 77 (2014) 457–466, https://doi.org/10.1016/j.
enbuild.2014.03.038.
[6] A. Nouri, J. Frisch, C. van Treeck, Metamodel-Based Global Sensitivity
Assessment of Building Energy Performance Simulations, 2023, https://doi.org/
10.2139/ssrn.4376427.
[7] E. Pantazis, D. Gerber, A framework for generating and evaluating façade designs
using a multi-agent system approach, Int. J. Architect. Comput. 16 (4) (2018)
248–270, https://doi.org/10.1177/1478077118805874.
[8] U. Knaack, T. Klein, M. Bilow, M, T. Auer, Facades Principles of Construction
Basel, Birkh¨
auser, 2007.
[9] L. Hendriks, H. Hens, Building Envelopes in a Holistic Perspective, Amsterdam,
Laboratorium Bouwfysica, Netherlands, 2000.
[10] G. Chen, D.J. Clements-Croome, An ANP Approach to the Assessment of Buildings
Façade Systems, Proceedings of the 2006 Whiteknights Workshop on MCDM,
2007. Reading, UK.
[11] S. Moghtadernejad, M. Saeed Mirza, L.E. Chouinard, Façade design stages: issues
and considerations, J. Architect. Eng. 25 (2019) 1, https://doi.org/10.1061/
(ASCE)AE.1943-5568.0000335.
[12] A.C. Keat, S. Das Adhikary, Blast-resistant Design of Façades, The Singapore
Engineer, 2014.
[13] C.M. Ramirez, A.B. Liel, J. Mitrani-Reiser, C.B. Haselton, A.D. Spear, J. Steiner, G.
G. Deierlein, E. Miranda, Expected earthquake damage and repair costs in
reinforced concrete frame buildings, Earthq. Eng. Struct. Dynam. 41 (11) (2012)
1455–1475, https://doi.org/10.1002/eqe.2216.
[14] IEA, Daylight in Buildings: A Source Book on Daylighting Systems and
Components, International Energy Agency Energy, Paris, 2000.
[15] D. Fernando, S. Navaratnam, P. Rajeev, J. Sanjayan, Study of technological
advancement and challenges of façade system for sustainable building: current
design practice, Sustainability 15 (2023), https://doi.org/10.3390/su151914319.
[16] E. Badino, L. Shtrepi, A. Astol, Acoustic performance-based design: a brief
overview of the opportunities and limits in current practice, Acoustics 2 (2)
(2020) 246–278, https://doi.org/10.3390/acoustics2020016.
[17] ISO 15686-2, Buildings and Constructed Assets — Service Life Planning — Part 2:
Service Life Prediction Procedures, ISO/TC 59/SC 14 Design life, 2012.
[18] S. Moghtadernejad, Design, Inspection, Maintenance, Life Cycle Performance and
Integrity of Building Facades, Dissertation, McGill University, Montreal, Canada,
2013.
[19] I. Burton, Report on reports: our common future: the world commission on
environment and development, environment, Sci. Pol. Sustain. Dev. 29 (1987)
25–29.
[20] R. Bach, N. Mohtashami, L. Hildebrand, Comparative overview on LCA software
programs for application in the façade design process, J. Facade Des. Eng. 7 (1)
(2019) 13–26, https://doi.org/10.7480/jfde.2019.1.2657.
[21] S. Bianchi, J. Ciurlanti, S. Pampanin, Comparison of traditional vs. low-damage
structural & non-structural building systems through a cost/performance-based
evaluation, Earthq. Spectra (2020) 1–20, https://doi.org/10.1177/
8755293020952445.
[22] R. Hartwell, S. Macmillan, M. Overend, Circular economy of façades: real-world
challenges and opportunities, Resources, Conserv. Recycl. 175 (2021), https://
doi.org/10.1016/j.resconrec.2021.105827.
[23] E. Voss, Q. Jin, M. Overend, A BPMN-based process map for the design and
construction of façades, J. Facade Des. Eng. 1 (2013) 17–29, https://doi.org/
10.3233/FDE-130006.
[24] T. Henriksen, S. Lo, U. Knaack, The impact of a new mould system as part of a
novel manufacturing process for complex geometry thin-walled GFRC, Architect.
Eng. Des. Manag. 12 (2016) 3, https://doi.org/10.1080/
17452007.2016.1159540.
[25] U. Bogenst¨
atter, Prediction and optimization of life-cycle costs in early design,
Build. Res. Inf. 28 (2000) 376–386, https://doi.org/10.1080/096132100418528.
[26] K. Zelenay, M. Perepelitza, D. Lehrer, High-performance Facades: Design
Strategies and Applications in North America and Northern Europe, Publication
No. CEC-500-99-013, California Energy Commission, Berkeley, CA, 2011.
[27] S. Ramachandran, An Integrated Computer Tool to Support Building Envelope
Design Process, Dissertation, Concordia University, Montreal, Canada, 2004.
[28] M. Donato, G. Zemella, G. Rapone, J. Hussain, C. Black, An innovative app for a
parametric, holistic and multidisciplinary approach to early design stages,
J. Facade Des. Eng. 5 (2) (2017) 113–127, https://doi.org/10.7480/
jfde.2017.2.1739.
[29] S. Kültür, N. Türkeri, U. Knaack, A holistic decision support tool for facade design,
Buildings 9 (8) (2019) 186, https://doi.org/10.3390/buildings9080186.
[30] A. Pracucci, S. Magnani, L. Vandi, O. Casadei, A. Uriarte, B. Bueno, M. Vavallo,
An analytical approach for the selection of technologies to be integrated in a
Fig. 9. Resilience denition, by referring to the description provided in Bru-
neau et al. [156].
S. Bianchi et al.
Building and Environment 250 (2024) 111184
17
Plug&play façade unit: the RenoZEB case study, Proceedings 65 (2020) 29,
https://doi.org/10.3390/proceedings2020065029.
[31] P. Vullo, A. Passera, R. Lollini, A. Prada, A. Gasparella, Implementation of a
multi-criteria and performance-based procurement procedure for energy
retrotting of facades during early design, Sustain. Cities Soc. 36 (2018)
363–377, https://doi.org/10.1016/j.scs.2017.09.029.
[32] S.U. Selvan, S.T. Saroglou, J. Joschinski, M. Calbi, V. Vogler, S. Barath, Y. Jacob
Grobman, Toward multi-species building envelopes: a critical literature review of
multi-criteria decision-making for design support, Build. Environ. 231 (2023),
https://doi.org/10.1016/j.buildenv.2023.110006.
[33] E. Kornyshova, C. Salinesi, MCDM Techniques Selection Approaches: State of the
Art, IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-
Making, 2007, pp. 22–29, https://doi.org/10.1109/MCDM.2007.369412.
[34] M. Bruen, Uptake and dissemination of multi-criteria decision support methods in
civil engineering—lessons from the literature, Appl. Sci. 11 (2021) 2940, https://
doi.org/10.3390/app11072940.
[35] T.L. Saaty, The Analytic Hierarchy Process, McGraw-Hill, New York, USA, 1980.
[36] C.L. Hwang, K. Yoon, Multi Atribute Decision Making: Methods and Applications:
A State of the Art Survey, Springer, Berlin/Heidelberg, Germany, 1981.
[37] A. Kaklauskas, E.K. Zavadskas, S. Raslanas, R. Ginevicius, A. Komka,
P. Malinauskas, Selection of low-e windows in retrot of public buildings by
applying multiple criteria method COPRAS: a Lithuanian case, Energy Build. 38
(5) (2006) 454–462, https://doi.org/10.1016/j.enbuild.2005.08.005.
[38] B. Roy, J.C. Hugonnard, Ranking of suburban line extension projects on the Paris
metro system by a multicriteria method, Transport. Res. Part A General 16 (1982)
301–312, https://doi.org/10.1016/0191-2607(82)90057-7.
[39] J.P. Brans, P. Vincke, Note-A preference ranking organisation method, Manag.
Sci. 31 (1985) 647–656, https://doi.org/10.1287/mnsc.31.6.647.
[40] E. Rey, Ofce building retrotting strategies: multicriteria approach of an
architectural and technical issue, Energy Build. 36 (4) (2004) 367–372, https://
doi.org/10.1016/j.enbuild.2004.01.015.
[41] E.K. Zavadskas, J. Antuchevicien, J. ˇ
Saparauskas, Z. Turskis, Multi-criteria
assessment of facades’ alternatives: peculiarities of ranking methodology, 11th
international conference on modern building materials, structures and
techniques, Procedia Eng. 57 (2013) 107–112, https://doi.org/10.1016/j.
proeng.2013.04.016.
[42] E.K. Zavadskas, Z. Turskis, J. Antucheviciene, A. Zakarevicius, Optimization of
weighted aggregated sum product assessment, Elektron Elektrotech 6 (2012) 3–6,
https://doi.org/10.5755/j01.eee.122.6.1810.
[43] K. Rogulj, N. Jajac, F. ˇ
Simi´
c, A Decision Support Concept for a construction design
project – selecting the type of glass façade, Croat. Oper. Res. Rev. 8 (2017)
333–350, https://doi.org/10.17535/crorr.2017.0021.
[44] P. Akbari, S.A. Yazdanfar, S.B. Hosseini, S. Norouzian-Maleki, Identication of
building façade functions by using fuzzy TOPSIS technique, Space Ontology
International Journal, Architecture, Urban Design and Planning 9 (1) (2020)
37–45. https://dorl.net/dor/20.1001.1.23456450.2020.9.1.3.5.
[45] C.T. Chen, Extensions of the TOPSIS for group decision-making under fuzzy
environment, Fuzzy Set Syst. 114 (1) (2000) 1–9, https://doi.org/10.1016/
S0165-0114(97)00377-1.
[46] Z.S. Moussavi Nadoushani, A. Akbarnezhad, J.F. Jornet, J. Xiao, Multi-criteria
selection of façade systems based on sustainability criterio, Build. Environ. 121
(2017) 67–78, https://doi.org/10.1016/j.buildenv.2017.05.016.
[47] Y.O. Elkhayat, M.G. Ibrahim, K. Tokimatsu, A.A.M. Ali, Multi-criteria selection of
high-performance glazing systems: a case study of an ofce building in New
Cairo, Egypt, J. Build. Eng. 32 (2020) 101466, https://doi.org/10.1016/j.
jobe.2020.101466.
[48] M.E. Demen
ť
eva, Algorithm for choosing a technology for insulating a building
facade during major repairs based on multi-criteria optimization, IOP Conf. Ser.
Mater. Sci. Eng. 1079 (2021) 052082, https://doi.org/10.1088/1757-899X/
1079/5/052082.
[49] K. Ogrodnik, Multi-criteria analysis of design solutions in architecture and
engineering: review of applications and a case study, Buildings 9 (12) (2019) 244,
https://doi.org/10.3390/buildings9120244.
[50] E. Ilter, O.C. Celik, A. Unlu, Multi-criteria performance evaluation of a glass panel
system using full-scale experimental data, Archit, Sci. Rev. 63 (6) (2020)
507–525, https://doi.org/10.1080/00038628.2020.1774861.
[51] T. Mukhamet, S. Kobeyev, A. Nadeem, S.A. Memon, Ranking PCMs for building
façade applications using multi-criteria decision-making tools combined with
energy simulations, Energy 215 (15) (2021) 119102, https://doi.org/10.1016/j.
energy.2020.119102.
[52] USGBC, LEED Green Associate Study Guide USGBC, 2009. Sacramento, CA.
[53] Z. Chen, An ANP approach to the assessment of buildings façade systems, in:
Proceedings of the 2006 Whiteknights Workshop on MCDM, 2006. Reading, UK.
[54] T.L. Saaty, Decision Making with Dependence and Feedback: the Analytic
Network Process, RWS Publications, Pittsburg, USA, 1996.
[55] S. Moghtadernejad, L.E. Chouinard, S.M. Mirza, Multi-criteria decision-making
methods for preliminary design of sustainable facades, J. Build. Eng. 19 (2018)
181–190, https://doi.org/10.1016/j.jobe.2018.05.006.
[56] G. Choquet, Theory of Capacities, Annales de l’institut Fourier, 1954.
[57] S. Moghtadernejad, S.M. Mirza, L.E. Chouinard, Determination of the fuzzy
measures for multicriteria and optimal design of a building façade using Choquet
integrals, J. Build. Eng. 26 (2019) 100877, https://doi.org/10.1016/j.
jobe.2019.100877.
[58] S. Moghtadernejad, L.E. Chouinard, S.M. Mirza, Enhanced façade design: a data-
driven approach for decision analysis based on past experiences, Dev. Built
Environ. 5 (2021) 100038, https://doi.org/10.1016/j.dibe.2020.100038.
[59] A. Le´
sniak, J. Balicki, Selection of façades nishing technology for a commercial
building using multi-criteria analysis, Entrepreneurial Business and Economics
Review 4 (2) (2016) 67–79.
[60] I. Yitmen, A. Al-Musaed, F. Yücelgazi, ANP model for evaluating the performance
of adaptive façade systems in complex commercial buildings, Engineering,
Construction and Architectural Management 29 (1) (2022) 431–455, https://doi.
org/10.1108/ECAM-07-2020-0559.
[61] A. Kolios, V. Mytilinou, E. Lozano-Minguez, K.A. Salonitis, Comparative study of
multiple-criteria decision-making methods under stochastic inputs, Energies 9
(2016) 566, https://doi.org/10.3390/en9070566.
[62] C.J. Hopfe, G.L.M. Augenbroe, J.L.M. Hensen, Multi-criteria decision making
under uncertainty in building performance assessment, Build. Environ. 69 (2013)
81–90, https://doi.org/10.1016/j.buildenv.2013.07.019.
[63] S. Oguztimur, Why Fuzzy Analytic Hierarchy Process Approach for Transport
Problems?, ERSA conference papers ersa, European Regional Science Association,
2011..
[64] E. Roszkowska, Multi Criteria Decision Making models by applying the TOPSIS
method to crisp and Interval data, Multiple Criteria Decision Making 6 (2011)
200–223.
[65] L. Kraujalien ˙
e, Comparative analysis of multicriteria decision-making methods
evaluating the efciency of technology transfer 17 (1) (2019) 72–93.
[66] W. De Keyser, P. Peeters, A note on the use of PROMETHEE multicriteria
methods, Eur. J. Oper. Res. 89 (3) (1996) 457–461, https://doi.org/10.1016/
0377-2217(94)00307-6.
[67] V. Machairas, A. Tsangrassoulis, K. Axarli, Algorithms for optimization of
building design: a review, Renew. Sustain. Energy Rev. 31 (2014) 101–112,
https://doi.org/10.1016/j.rser.2013.11.036.
[68] A. L´
opez Jaimes, S.Z. Martinez, C.A. Coello Coello, An Introduction to Multi-
Objective Optimization Techniques, Nova Science Publishers Inc., 2009.
[69] M. Wetter, GenOpt® Generic Optimization Program User Manual, 2008.
Berkeley, CA.
[70] J.H. Holland, Adaptation in Natural and Articial Systems, The University of
Michigan press, Ann Arbor, US, 1975.
[71] K. Deb, S. Agrawal, A. Pratap, T. Meyarivan, A Fast Elitist Nondominated Sorting
Genetic Algorithm for Multi-Objective Optimization: NSGA-II, Parallel Problem
Solving from Nature, 2000. Berlin, DE.
[72] E. Zitzler, M. Laumanns, L. Thiele, SPEA2: Improving the Strength Pareto
Evolutionary Algorithm, Evolutionary Methods for Design, Optimization and
Control with Applications to Industrial Problems, 2001. Athens, Greece.
[73] C.A. Coello Coello Coello, G. Toscano Pulido, A Micro-genetic Algorithm for
Multiobjective Optimization, Evolutionary Multi-Criterion Optimization, Berlin,
DE, 1993, https://doi.org/10.1007/3-540-44719-9_9.
[74] C. Karagkouni (Sandy), A. Fatah gen Schieck, M. Tsigkari, A. Chronis,
Performance-driven facades: analysis of natural cross-ventilation in an indoor
environment with Fast Fluid Dynamics and apertures optimization based on a
genetic algorithm, Simulation: Transactions of the Society for Modeling and
Simulation International 90 (2014) 8, 978–99010.1177/0037549714537133.
[75] N. Yoon, M.A. Piette, J.M. Han, W. Wu, A. Malkawi, Optimization of window
positions for wind-driven natural ventilation performance, Energies 13 (10)
(2020) 2464, https://doi.org/10.3390/en13102464.
[76] D. Tuhus-Dubrow, M. Krarti, Genetic-algorithm based approach to optimize
building envelope design for residential buildings, Build. Environ. 45 (7) (2010)
1574–1581, https://doi.org/10.1016/j.buildenv.2010.01.005.
[77] X. Su, X. Zhang, Environmental performance optimization of window–wall ratio
for different window type in hot summer and cold winter zone in China based on
life cycle assessment, Energy Build. 42 (2) (2010) 198–202, https://doi.org/
10.1016/j.enbuild.2009.08.015.
[78] J. Gagne, M. Andersen, A generative facade design method based on daylighting
performance goals, J. Build. Perform. Simul. 5 (3) (2012) 141–154, https://doi.
org/10.1080/19401493.2010.549572.
[79] P. Nadiri, M. Mahdavinejad, P. Pilechiha, Optimization of building façade to
control daylight excessiveness and view to outside, J. Appl. Eng. Sci. 9 (2) (2019)
161–168, https://doi.org/10.2478/jaes-2019-0022.
[80] Y. Li, C. Huang, G. Zhang, J. Yao, Machine learning modelling and genetic
optimization of adaptove building facade towards the light environment, Int.
Conf. Assoc. Computer-Aided Arch. Design Res. 1 (2022) 141–150.
[81] S.L. Torres, Y. Sakamoto, Facade design optimization for daylight with a simple
genetic algorithm, Proc. Build. Simul. (2007).
[82] S. Marzban, L. Ding, F. Fiorito, An evolutionary approach to single-sided
ventilated façade design, Procedia Eng. 180 (2017) 582–590, https://doi.org/
10.1016/j.proeng.2017.04.217.
[83] L. Caldas, GENE_ARCH: an Evolution-Based Generative Design System for
Sustainable Architecture, Intelligent Computing in Engineering and Architecture,
2006, https://doi.org/10.1007/11888598_12. Berlin, DE.
[84] C. Kasinalis, R.C.G.M. Loonen, D. C´
ostola, J.L.M. Hensen, Framework for
assessing the performance potential of seasonally adaptable facades using multi-
objective optimization, Energy Build. 79 (2014) 106–113, https://doi.org/
10.1016/j.enbuild.2014.04.045.
[85] T. M´
endez Echenagucia, A. Capozzoli, Y. Cascone, M. Sassone, The early design
stage of a building envelope: multi-objective search through heating, cooling and
lighting energy performance analysis, Appl. Energy 154 (2015) 577–591, https://
doi.org/10.1016/j.apenergy.2015.04.090.
[86] H. Kim, M.J. Clayton, A multi-objective optimization approach for climate-
adaptive building envelope design using parametric behavior maps, Build.
Environ. 185 (2020) 107292, https://doi.org/10.1016/j.buildenv.2020.107292.
S. Bianchi et al.
Building and Environment 250 (2024) 111184
18
[87] S. Wen, X. Ju, G. Hua, P. Xue, D. Lai, Comparing the performance of four shading
strategies ase don a multi-objective genetic algorithm: a case study in a university
library, J. Build. Eng. 1 (2023), https://doi.org/10.1016/j.jobe.2022.105532.
[88] R. Shan, Optimization for heating, cooling and lighting load in building façade
design, Energy Proc. 57 (2014) 1716–1725, https://doi.org/10.1016/j.
egypro.2014.10.142.
[89] M.D. Yang, M.D. Lin, Y.H. Lin, K.T. Tsai, Multiobjective optimization design of
green building envelope material using a non-dominated sorting genetic
algorithm, Appl. Therm. Eng. 111 (2017) 1255–1264, https://doi.org/10.1016/j.
applthermaleng.2016.01.015.
[90] F.P. Chantrelle, H. Lahmidi, W. Keilholz, M. El Mankibi, P. Michel, Development
of a multicriteria tool for optimizing the renovation of buildings, Appl. Energy 88
(4) (2011) 1386–1394, https://doi.org/10.1016/j.apenergy.2010.10.002.
[91] Q. Jin, M. Overend, A prototype whole-life value optimization tool for façade
design, J. Build. Perform. Simul. 7 (3) (2014) 217–232, https://doi.org/10.1080/
19401493.2013.812145.
[92] J.T. Jin, J.W. Jeong, Optimization of a free-form building shape to minimize
external thermal load using genetic algorithm, Energy Build. 85 (2014) 473–482,
https://doi.org/10.1016/j.enbuild.2014.09.080.
[93] L.N. Moraes, F.O.R. Pereira, Design Process for Optimization of Buildings Facades
for Solar Irradiation in the Brazilian Context, CISBAT 2015, 2015. Lausanne,
Switzerland.
[94] A. Zhang, R. Bokel, A. van den Dobbelsteen, Y. Sun, Q. Huang, Q. Zhang,
Optimization of thermal and daylight performance of school buildings based on a
multi-objective genetic algorithm in the cold climate of China, Energy Build. 139
(2017) 371–384, https://doi.org/10.1016/j.enbuild.2017.01.048.
[95] F. Fathy, H.A. Fareed, Performance-driven Façade Design Using an Evolutionary
Multi-Objective Optimization Approach, International Conference for Sustainable
Design of the Built Environment- SDBE, London, UK, 2017.
[96] Y. Fang, S. Cho, Design optimization of building geometry and fenestration for
daylighting and energy performance, Sol. Energy 191 (2019) 7–18, https://doi.
org/10.1016/j.solener.2019.08.039.
[97] Z. Fan, M. Liu, S. Tang, A multi-objective optimization design method for
gymnasium facade shading ratio integrating energy load and daylight comfort,
Build. Environ. 207 (2022) 108527, https://doi.org/10.1016/j.
buildenv.2021.108527.
[98] S. Nazari, B. Sajadi, I. Sheikhansari, Optimisation of commercial buildings
envelope to reduce energy consumption and improve indoor environmental
quality (IEQ) using NSGA-II algorithm, Int. J. Ambient Energy 44 (2023) 1,
https://doi.org/10.1080/01430750.2022.2157482.
[99] Z. Jalali, E. Noorzai, S. Heidari, Design and optimization of form and facade of an
ofce building using the genetic algorithm, Sci. Technol. Built Environ. 26 (2)
(2020) 128–140, https://doi.org/10.1080/23744731.2019.1624095.
[100] J. Montali, M. Sauchelli, Q. Jin, M. Overend, Knowledge-rich optimisation of
prefabricated façades to support conceptual design, Autom. ConStruct. 97 (2019)
192–204, https://doi.org/10.1016/j.autcon.2018.11.002.
[101] R. Evins, P. Pointer, R. Vaidyanathan, Multi-objective Optimization of the
Conguration and Control of a Double-Skin Facade, 12th Conference of
International Building Performance Simulation Association, 2011. Sydney.
[102] J. Kennedy, R. Eberhart, Particle swarm optimization, in: Proceedings of ICNN’95
- International Conference on Neural Networks, 1995, https://doi.org/10.1109/
ICNN.1995.488968.
[103] R. Hooke, T.A. Jeeves, “Direct search” solution of numerical and statistical
problems, J. ACM 8 (2) (1961) 212–229, https://doi.org/10.1145/
321062.321069.
[104] B. Lartigue, B. Lasternas, V. Loftness, Multi-objective optimization of building
envelope for energy consumption and daylight, Indoor Built Environ. 23 (1)
(2014) 70–80, 10.1177%2F1420326X13480224.
[105] Y. Yılmaz, B.C. Yılmaz, Life cycle cost optimization of building façade: a social
housing case, Indoor Built Environ. 30 (2) (2021) 215–228, https://doi.org/
10.1177/1420326X19889991.
[106] A. Hani, T.A. Koiv, Optimization of ofce building façades in a warm summer
continental climate, Smart Grid Renew. Energy 3 (2012) 222–230, https://doi.
org/10.4236/sgre.2012.33031.
[107] J. Joe, W. Choi, Y. Kwak, J.H. Huh, Optimal design of a multi-story double skin
facade, Energy Build. 76 (2014) 143–150, https://doi.org/10.1016/j.
enbuild.2014.03.002.
[108] I. Chatzikonstantinou, B. Ekici, I.S. Sariyildiz, B.K. Koyunbaba, Multi-objective
diagrid facade optimization using differential evolution, in: Proceedings of the
IEEE Congress on Evolutionary Computation (CEC), Sendai, Japan, 2015.
Piscataway, USA.
[109] R. Storn, K. Price, Differential evolution–a simple and efcient heuristic for global
optimization over continuous spaces, J. Global Optim. 11 (1997) 341–359,
https://doi.org/10.1023/A:1008202821328.
[110] R.W. Schulze-Riegert, M. Krosche, A. Fahimuddin, S.G. Ghedan, Multi-objective
optimization with application to model validation and uncertainty quantication,
in: SPE Middle East Oil and Gas Show and Conference, 2007, https://doi.org/
10.2118/105313-MS. Manama, Bahrain.
[111] K. Krishnakumar, Micro-Genetic algorithms for stationary and non-stationary
function optimization, in: Proc. SPIE 1196, Intelligent Control and Adaptive
Systems, 1990, https://doi.org/10.1117/12.969927.
[112] T. Robiˇ
c, B. Filipiˇ
c, Differential Evolution versus Genetic Algorithms in
Multiobjective Optimization, 4th International Conference on Evolutionary Multi-
Criterion Optimization, Matsushima, Japan, 2007.
[113] J. TvrdíK, Competitive Differential Evolution, MENDEL’06, 12th International
Conference on Soft Computing, 2006.
[114] L. Ming, D. Wenqiang, N. Fuzhong, An adaptive Particle swarm optimization
algorithm based on directed weighted complex network. https://doi.org/10
.1155/2014/434972, 2014.
[115] S. Hassoun, Fundamentals of Articial Neural Networks, 1995.
[116] D. Gossard, B. Lartigue, F. Thellier, Multi-objective optimization of a building
envelope for thermal performance using genetic algorithms and articial neural
network, Energy Build. 67 (2013) 253–260, https://doi.org/10.1016/j.
enbuild.2013.08.026.
[117] S. Marzban, L. Ding, V. Timchenko, M. Irger, Façade optimization in a wind-
driven ventilated residential building targeting thermal comfort, IAQ and energy
consumption, Int. J. Environ. Sustain Dev. 7 (2016) 5, https://doi.org/10.7763/
IJESD.2016.V7.804.
[118] M. Minaei, A. Aksamija, A framework for performance-based façade design:
approach for multi-objective and automated simulation and optimization, Build.
Technol. Educator’s Soc. 40 (2019), https://doi.org/10.7275/ye7w-nh09.
[119] Z. Han, X. Li, J. Sun, M. Wang, G. Liu, An interactive multi-criteria decision-
making method for building performance design, Energy Build. 282 (2023),
https://doi.org/10.1016/j.enbuild.2023.112793.
[120] I. Jaffal, Physics-informed machine learning for metamodeling thermal comfort in
non-air-conditioned Buildings, Build. Simulat. 16 (2023) 299–316, https://doi.
org/10.1007/s12273-022-0931-y.
[121] L. Duc Long, An AI-driven model for predicting and optimizing energy-efcient
building envelopes, Alex. Eng. J. 79 (2023) 480–501, https://doi.org/10.1016/j.
aej.2023.08.041.
[122] R.S. Sutton, A.G. Barto, Reinforcement Learning: an Introduction, MIT press,
Cambridge, MA, 2018.
[123] M. Han, R. May, X. Zhang, et al., A novel reinforcement learning method for
improving occupant comfort via window opening and closing, Sustain. Cities Soc.
61 (2020) 102247, https://doi.org/10.1016/j.scs.2020.102247.
[124] J. Park, T. Dougherty, H. Fritz, Z. Nagy, LightLearn: an adaptive and occupant
centered controller forlighting based on reinforcement learning, Build. Environ.
147 (2019) 397–414, https://doi.org/10.1016/j.buildenv.2018.10.028.
[125] M. Oprea, Applications of Multi-Agent Systems, IFIP International Federation for
Information Processing, Springer, Boston, 2004, https://doi.org/10.1007/1-
4020-8159-6_9.
[126] D.J. Gerber, E. Pantazis, A. Wang, A multi-agent approach for performance based
architecture: design exploring geometry, user, and environmental agencies in
façades, Autom. ConStruct. 76 (2017) 45–58, https://doi.org/10.1016/j.
autcon.2017.01.001.
[127] E. Pantazis, D. Gerber, A framework for generating and evaluating façade designs
using a multi-agent system approach, Int. J. Architect. Comput. 16 (4) (2018)
248–270, https://doi.org/10.1177/1478077118805874.
[128] A. Radford, L. Metz, S. Chintala, Unsupervised Representation Learning with Deep
Convolutional Generative Adversarial Networks, 2016, https://doi.org/
10.48550/arXiv.1511.06434.
[129] Q. Yu, J. Malaeb, W. Ma, Architectural Facade Recognition and Generation
through Generative Adversarial Networks, International Conference on Big Data
& Articial Intelligence & Software Engineering, Bangkok, TH, 2020.
[130] C. Sun, Y. Zhou, Y. Han, Automatic generation of architecture facade for historical
urban renovation using generative adversarial network, Build. Environ. 212
(2022) 108781, https://doi.org/10.1016/j.buildenv.2022.108781.
[131] S. Muin, S. Gunay, K. Mosalam, Decision Making of Innovative Building Facade in
Singapore, Second International Conference on Performance-based and Lifecycle
Structural Engineering, Brisbane, AU, 2015.
[132] S. Homaei, M. Hamdy, A robustness-based decision making approach for multi-
target high performance buildings under uncertain scenarios, Appl. Energy 267
(2020) 114868, https://doi.org/10.1016/j.apenergy.2020.114868.
[133] R. Moschetti, S. Homaei, E. Taveres-Cachat, S. Grynning, Assessing responsive
building envelope designs through robustness-based multi-criteria decision
making in zero-emission buildings, Energies 15 (2022) 1314, https://doi.org/
10.3390/en15041314.
[134] J.W. Kruisselbrink, Robust Optmization, 2008.
[135] S. Chang, D. Castro-Lacouture, Y. Yamagata, Decision support for retrotting
building envelopes using multi-objective optimization under uncertainties,
J. Build. Eng. 32 (2020) 101413, https://doi.org/10.1016/j.jobe.2020.101413.
[136] C.J. Hopfe, M.T.M. Emmerich, R. Marijt, J.L.M. Hensen, Robust Multi-Criteria
Design Optimization in Building Design, First IBPSA-Engeland conference
Building Simulation and Optimization, Loughborough, UK, 2012.
[137] Y. Li, N. Nord, N. Zhang, C. Zhou, An ANN-based optimization approach of
building energy systems: case study of swimming pool, J. Clean. Prod. 277 (2020)
124029, https://doi.org/10.1016/j.jclepro.2020.124029.
[138] R. Snaiki, S.S. Parida, Climate change effects on loss assessment and mitigation of
residential buildings due to hurricane wind, J. Build. Eng. 69 (2023) 106256,
https://doi.org/10.1016/j.jobe.2023.106256.
[139] J. Ciurlanti, G. Milan, J. Dennis, S. Bianchi, G. Lori, G. Manara, M. Overend, D. N.
Grant Sensitivity Analysis and Risk Assessment of Unitised Glass Curtain Walls,
SECED 2023 Conference, Cambridge, United Kingdom..
[140] S. Bianchi, J. Ciurlanti, M. Overend, S. Pampanin, A probabilistic-based
framework for the integrated assessment of seismic and energy economic losses of
Buildings, Eng. Struct. 269 (2022) 4, https://doi.org/10.1016/j.
engstruct.2022.114852.
[141] F. Braga, V. Manfredi, A. Masi, A. Salvatori, M. Vona, Performance of non-
structural elements in RC buildings during the L’Aquila, 2009 earthquake, Bull.
Earthq. Eng. 9 (2010) 307–324, https://doi.org/10.1007/s10518-010-9205-7.
S. Bianchi et al.
Building and Environment 250 (2024) 111184
19
[142] A. Baird, A. Palermo, S. Pampanin, Facade damage assessment of multi-storey
buildings in the 2011 Christchurch earthquake, Bull. N. Z. Soc. Earthq. Eng. 44 (4)
(2011) 368–376, https://doi.org/10.1002/suco.201100040.
[143] S.Pampanin S, Reality-check and renewed challenges in earthquake engineering:
implementing low-damage structural systems – from theory to practice, Bull. NZ
Soc. Earthq. Eng. 45 (4) (2012) 137–160, https://doi.org/10.5459/
bnzsee.45.4.137-160.
[144] Bianchi, S. Pampanin, Fragility functions for architectural non-structural
components. ASCE, J. Struct. Eng., https://doi.org/10.1061/(ASCE)ST.1943-541
X.0003352..
[145] C. Menna, C. Del Vecchio, M. Di Ludovico, G.M. Mauro, F. Ascione, A. Prota,
Conceptual design of integrated seismic and energy retrot interventions,
J. Build. Eng. 38 (2021) 102190, https://doi.org/10.1016/j.jobe.2021.102190.
[146] M. Caruso, R. Pinho, F. Bianchi, F. Cavalieri, M.T. Lemmo, Integrated economic
and environmental building classication and optimal seismic vulnerability/
energy efciency retrotting, Bull. Earthq. Eng. 19 (2021) 3627–3670, https://
doi.org/10.1007/s10518-021-01101-4.
[147] CURT, Collaboration, Integrated Information and the Project Life Cycle in
Building Design, Construction and Operation, Construction Users Roundtable -
CURT, Cincinnati, USA, 2004.
[148] Y.W. Lim, T.E. Seghier, M.F. Harun, M.H. Ahmad, A.A. Samah, H.A. Majid,
Computational BIM for building envelope sustainability optimization, in: MATEC
Web Conference, vol. 278, 2019 04001, https://doi.org/10.1051/matecconf/
201927804001.
[149] S. Asgari, S. Haghir, E. Noorzai, Reducing energy consumption in operation and
demolition phases by integrating multi-objective optimization with LCA and BIM,
Energy Efc. 16 (2023) 54, https://doi.org/10.1007/s12053-023-10129-1.
[150] Z. Chen, A.W.A. Hammad, I. Kamardeen, A. Haddad, Optimising window design
on residential building facades by considering heat transfer and natural lighting
in nontropical regions of Australia, Buildings 10 (2020) 206, https://doi.org/
10.3390/buildings10110206.
[151] V. Sangiorgio, S. Martiradonna, F. Fatiguso, I. Lombillo, Augmented reality based
- decision making (AR-DM) to support multi-criteria analysis in constructions,
Autom. ConStruct. 124 (2021) 103567, https://doi.org/10.1016/j.
autcon.2021.103567.
[152] European Environment Agency, Economic Losses and Fatalities from Weather-
and Climate-Related Events in Europe, Report, 2022.
[153] S. Hosseini, K. Barker, J.E. Ramirez-Marquez, A review of denitions and
measures of system resilience, Reliab. Eng. Syst. Saf. 145 (2016) 47–61, https://
doi.org/10.1016/j.ress.2015.08.006.
[154] R.S. Athauda, A.S. Asmone, S. Conejos, Climate change impacts on facade
building materials: a qualitative study, Sustainability 15 (10) (2023) 7893,
https://doi.org/10.3390/su15107893.
[155] L.W. Chew, X.-X. Li, M.Y.L. Chew, Climate change projection and its impacts on
building façades in Singapore, Sustainability 15 (4) (2023) 3156, https://doi.org/
10.3390/su15043156.
[156] S. Homaei, M. Hamdy, Thermal resilient buildings: how to be quantied? A novel
benchmarking framework and labelling metric, Build. Environ. 201 (2021),
https://doi.org/10.1016/j.buildenv.2021.108022.
[157] E. Elnagar, A. Zeoli, R. Rahif, S. Attia, V. Lemort, A qualitative assessment of
integrated active cooling systems: a review with a focus on system exibility and
climate resilience, Renew. Sustain. Energy Rev. 175 (2023), https://doi.org/
10.1016/j.rser.2023.113179.
[158] A. Figliola, Digital workow for climate resilient building façade generation,
Build. Res. Inf. 51 (3) (2023) 257–278, https://doi.org/10.1080/
09613218.2022.2121907.
[159] M. Bruneau, S. Chang, R. Eguchi, et al., A framework to quantitatively assess and
enhance the seismic resilience of communities, Earthq. Spectra 19 (4) (2003)
733–752, 10.1193%2F1.1623497.
S. Bianchi et al.
