







































MF. Al-Kazee et al. /Future Technology                                                                                          May 2025| Volume 04 | Issue 02 | Pages 
30-40 

30 

 

 

 

Article 

Reframing the early-stage design process of 

residential buildings based on an energy-efficient, 

designerly decision support system (DDSS) 
Mohamed Faisal Al-Kazee1*, Samad Negin Taji2, Tahereh Nasr3, Reza Mansoori4, 

Mohammadjavad Mahdavinejad1 

1College of Engineering and Architecture, University of Nizwa, Oman 
2Department of Architecture, Faculty of Art and Architecture, Tarbiat Modares University, Tehran, Iran 
3Department of Architecture, Shiraz Branch, Islamic Azad University, Shiraz, Iran 
4Department of Architecture, Faculty of Engineering, Ilam University, Ilam, Iran 

A R T I C L E   I N F O 
 

Article history: 
Received 24 February 2025  
Received in revised form 
03 April 2025 
Accepted 14 April 2025 
 
Keywords:  
Designerly decision support system (DDSS), 
Visual-thermal comfort, Designerly approach, 
High-performance architecture, Mitigating climate 
change, Energy efficiency  
 
*Corresponding author 
Email address: 
faisal.kazey@unizwa.edu.om  
 
DOI: 10.55670/fpll.futech.4.2.4 

A B S T R A C T 
 

The literature emphasizes the role of the early-stage design process, 
particularly early design decisions related to mid-rise residential buildings. On 
the other hand, the futuristic concepts of high-performance architecture 
represent a paradigm shift that requires a data-conscious approach to climate 
change mitigation. This research adopts a designer approach to address the 
complex and ill-defined sci-tech problems within the architectural field. The 
study aims to develop a framework for a user-friendly, data-driven Designerly 
Decision Support System (DDSS) to categorize and automate the architectural 
design process, with a particular focus on the early design stage. The 
methodology is based on in-depth structured interviews with architects to 
identify and classify influential parameters in the early design stages. These 
parameters were extracted to construct a metamodel. Subsequently, sensitivity 
analysis was employed to investigate the background of key performance 
metrics and the relationships among them. The research calculates the energy 
loads of nine mid-rise residential building patterns in Tehran using Energy Plus 
software. Based on the quantitative results, three representative patterns—1) 
high-consumption, 2) low-consumption, and 3) mid-rise—were selected for 
further sensitivity analysis. The findings indicate that a reference database can 
be created to comprehensively guide designers working on mid-rise residential 
patterns. This database can also serve as a resource for revising urban planning 
guidelines with energy metrics in mind. Additionally, the north and south 
Window-to-Wall Ratios (WWRs) are identified as the most significant design 
parameters, directly and interactively influencing heating, cooling, and lighting 
functions. 

1. Introduction 

Nowadays, a significant portion of environmental 
problems in cities- especially in developing countries- is 
linked to the construction industry and the growing demand 
for water and energy. It is essential to integrate performance 
simulation into the design process [1]. Therefore, water–
energy efficiency has become one of the highest priorities in 
the field, particularly in the decade following 2020 and 
notably in developing countries [2]. To enhance simulation 
use in design, strategies include using reliable data, defining 
performance criteria, and framing relevant performance 
questions [3-5]. A 3D approach is required in using simulation 

tools in the design process [6, 7], design energy simulation for 
architects [8-10], and simulation optimization. Focusing on 
early-stage design decisions is a way to achieve sustainable 
buildings at lower costs [11, 12] and improve the energy 
performance of residential buildings [13-15]. A designer 
approach to sci-tech issues, as elaborated in high-
performance architecture theory, is rooted in the core of 
'design thinking' and its application [16]. Recognizing the 
contributions of building performance simulations and 
architects is crucial in the context of climate change 
mitigation. A significant body of literature focuses on a 
designerly approach to sustainability [17, 18], especially 
within decision-making processes, reflecting a shift toward  

 

 

Future Technology 

Open Access Journal 

https://doi.org/10.55670/fpll.futech.4.2.4 

 

 

 

May 2025| Volume 04 | Issue 02 | Pages 30-40 

Journal homepage: https://fupubco.com/futech 

 

ISSN 2832-0379 

mailto:faisal.kazey@unizwa.edu.om
https://doi.org/10.55670/fpll.futech.4.2.4
https://fupubco.com/futech


MF. Al-Kazee et al. /Future Technology                                                                                          May 2025| Volume 04 | Issue 02 | Pages 30-40 

31 

 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

future-oriented building concepts- commonly termed 
“Zukunft Bau” at the age of energy resource scarcity [19, 20]. 
The literature utilizes sensitivity analysis to evaluate 
upgrades to basic building geometry [21, 22], aiming to 
enhance energy efficiency [23] alongside Building 
Performance Simulations (BPS) [24] in developing countries 
such as Iran [25, 26]. As Bryan Lawson discusses in How 
Designers Think [27], architecture involves form finding and 
shape grammar [28, 29], providing a simplified approach for 
evaluating building sustainability [30]. 

Therefore, it is crucial that evaluation and design 
processes are adapted to local conditions rather than relying 
exclusively on internationally standardized methods [30]. In 
a developing country like Iran- where per capita energy 
consumption in the construction sector is four times higher 
than in Europe, and over 98% of building energy use is reliant 
on fossil fuels [2]- the design of energy-efficient buildings 
becomes a matter of critical importance. Given the nascent 
stage of technical and executive knowledge of sustainability 
in building design, focusing on contextual design 
methodologies is both necessary and rational. This approach 
can inform the development of regulations, policymaking, and 
strategies tailored to local conditions, thereby effectively 
addressing energy challenges within the construction sector. 

The article aims to reframe the early-stage design 
process of residential buildings with a focus on energy 
efficiency to develop a designerly decision support system 
(DDSS) for future buildings. Therefore, the main objective of 
the research is to identify architects' preferences and 
prioritize parameters influencing energy efficiency in the 
early stages of architectural design. Therefore, the main 
approach of the article is to create a user-friendly framework, 
which is to be addressed and emphasized in the design 
process. The effect of building aspect ratio on energy 
efficiency [31], nature-based solutions [32], and simulation-
based optimization methods along with data-driven 
integrated design [33-35] have been explored. Additionally, 
factors such as the proportions of the form [34], overall 
building form [36], macroparameters [37], facade geometry 
[38], building envelope components [39], and courtyard 
dimensions have been identified as significant [40].  

Donald A. Schön explains that architectural design, in 
essence, involves framing, which transforms the design 
process into abstract elements and examines the 
relationships between them [41]. In this model, by asking 
what something is and how it adds value to the design 
process, a set of performance requirements is defined to serve 
as a benchmark for supporting decision-making and guiding 
design in the process of common housing patterns. 
Metamodels are suitable for use in the early stages of the 
design process when a general comparison of options in 

terms of performance is more important than precise 
estimation [22]. In the next step, the necessary capacity for 
strategic thinking is obtained using sensitivity analysis based 
on the model and data from the previous stage. Sensitivity 
analysis is a valuable method for identifying design 
parameters that need attention in the design process [34]. 
This study emphasizes the early stages of the architectural 
design process- specifically the conceptual and preliminary 
design phases- by examining the extent of architects’ roles 
and their influence on key energy-related design decisions. 

2. Theoretical framework  

2.1 Energy-efficient building design 
The stages of the energy-efficient building design 

process, or in a designerly approach to responsive design for 
advanced building simulation [42-45], vary according to 
different researchers. However, it seems that there is 
considerable consensus among different models. In this 
article, the stages of the design process are divided into three 
main sections: pre-design (design planning), three design 
stages (conceptual design, preliminary design, detailed 
design), and post-design (construction and application).  
Conceptual Planning: The starting point of any architectural 
project is the planning for design, where the overall project 
requirements are defined. In this stage, energy objectives and 
strategies guiding future design decisions are considered [44, 
45].  
Preliminary design: The final geometry of the building is 
determined, and building materials and envelope are defined. 
In this stage, the findings of conceptual design are integrated 
with relevant information about interior geometries and 
building envelope specifications [32,39], and various 
combinations of energy performance components are 
evaluated in an iterative process.  
Detailed design: This is the last stage of design where 
construction drawings are produced and economic criteria 
are considered. Final considerations regarding finishes and 
dimensions of interior spaces are made [18]. An architect 
should consider all factors, including evaluating the effects of 
different tree species on enhancing outdoor thermal comfort 
[46], as well as the relationship between plan and space [47, 
48], to improve building performance. Categorically, building 
components are divided into five main groups: building form, 
window system, shading system, roof, and cladding (including 
plan and space) [47-51]. Zhao and de Angelis [47] categorized 
design parameters into three main sections: architectural 
aspects of the building (plan, envelope, and form), the 
building-site relationship, and the building plan along with its 
equipment system. Parameters related to each of these 
sections have been derived from previous studies (Figure 1).  

 
2.2 Formulating the alignment of energy simulation with 

the initial stages of the energy-efficient building 
design process 
One of the essential aims of this research is to align 

energy simulation with the initial stages of the energy-
efficient building design process. The first step in any energy 
analysis is formulating questions related to performance [1]; 
in fact, it formulates a general model of the design process 
[36]. Models developed to support designers' decision-
making are generally based on three questions: "know-why" 
[40], "what-if" [34], or "if-then". In this formula [16], the focus 
shifts from the problem space to the solution space, and the 
designer must pursue value.  

 
 

Abbreviations 

BPS   Building Performance Simulations 

CSA   Clear Surface Area 

DDSS   Designerly decision support system 

FSA   Frame Surface Area 

GBI   Green Building Index 

PV   Photovoltaic  

NWWR   Net Window-to-Wall Ratio 

Sobol   Variance-Based Sensitivity Analysis  

SWWR   Solid Window-to-Wall Ratio 

WSA   Window Surface Area 

WTA   Window-to-Total Area ratio 

WWR   Window-to-Wall Ratio 

 

 

 

 

 

 

 

 

 



MF. Al-Kazee et al. /Future Technology                                                                                          May 2025| Volume 04 | Issue 02 | Pages 30-40 

32 

 

 
 
 

Metamodel: Metamodel, or surrogate models, are simplified 
models of complex models whose purpose is to approximate 
the behavior of the entire system and the relationships 
between variables; they can also provide an image and 
description of the design space to the designer [4, 10, 22]. 
Metamodels enable quicker exploration of design options by 
offering approximate yet cost-effective alternatives to full 
simulations, reducing both computation time and resource 
use. Sensitivity analysis: Sensitivity analysis should be an 
integral part of any solution because the understanding of a 
solution's state cannot be achieved without the information 
obtained from sensitivity analysis [9, 20, 21, 35]. Sensitivity 
analysis is perhaps the most useful and widely used method 
available to support decision-makers. Sensitivity analysis can 
answer "what if" questions [43] through regression analysis 
or correlation coefficients. 

3. Research methodology 

3.1 In-depth interview 
Research on the use of in-depth interviews in emerging 

areas of architecture [52-56] highlights the importance of 
minimizing input variables in energy simulation for efficient 
building design [19]. Experienced architects’ expertise allows 
them to recognize scalable actions [36], which in turn 
facilitates the identification of scalable parameters during the 
early design stages. The aim of conducting interviews with 
architects was to extract key parameters that could serve as 
the foundation for developing a generalized model of 
prevalent architectural patterns.  

3.2 Simulation tools  
The software was developed using Google SketchUp with 

the Open Studio plugin (v1.0.0) to support early-stage design 
and informed decision-making. Energy Plus was used for 
simulation, while jEPlus facilitated parametric studies by 
modifying Energy Plus input files. For sensitivity analysis, 
Simlab- validated and developed by the European 
Commission- was used, leveraging Monte Carlo methods for 
uncertainty and sensitivity assessments. 

 

 

  
3.3 Research materials   

Minimizing heating and cooling energy consumption 
while maximizing daylight use were defined as the primary 
objective functions. Given that each objective does not 
contribute equally to overall energy reduction [57], it was 
essential to assign specific scores and weights to them. This 
comprehensive scoring approach enables effective 
comparison among multiple design alternatives and supports 
the interpretation of sensitivity analysis results. In this study, 
weights of 40%, 40%, and 20% were assigned to heating, 
cooling, and daylight performance, respectively. Additionally, 
a daylight assessment method was required that correlates 
both with energy consumption and natural lighting quality. 
Therefore, the worst point in the plan area in terms of average 
illuminance in lux between 8 am and 6 pm throughout the 
year  was considered as the criterion for action [58]. 
Determining this point was done by placing sensors at a 
height of one meter in the form of a grid across the plan area. 
After identifying the darkest point in the room using this 
method, the electric lighting system was defined to turn on 
with less than 300 lux of natural light and remain off at other 
times.). The objective function aims to identify a design 
output that maximizes daylight illuminance throughout the 
year while reducing heating and cooling loads throughout the 
year. 

3.4 Case studies  
The case study focuses on typical mid-rise residential 

typologies identified in the literature. Geographically, it is 
located between 35°34′ and 35°51′ north latitude. The 
analysis is conducted in accordance with Volume 19 of the 
National Building Code, which addresses energy conservation 
in buildings. In terms of energy efficiency, the selected case 
represents a building type that requires moderate energy-
saving measures. The most prevalent form of urban 
residential development consists of mid-rise apartment 
blocks. According to the building codes, high-rise buildings 
are defined as those exceeding 23 meters in height, with 
specifications outlined in Table 1 [5]. 

 
 

 

Building Development 

 

Building Adjacent 

Roof 

Side Surfaces 

Floor  

Ceiling  

Plan 

Exterior Shell 

Building Shape 

Architectural Design Parameters 

Utilities     Building Structure   Context of Design  

Building Plan 

 

Instalation 

System 

Figure 1. Parameters of architectural design for energy-efficient buildings 



MF. Al-Kazee et al. /Future Technology                                                                                          May 2025| Volume 04 | Issue 02 | Pages 30-40 

33 

 

Table 1. Specifications of the zones in the Tehran City Masterplan  

 
 
 
 
 
 
 
Tehran is a sample of the BSk climate. With this situation, 

the designer faces the possibility of encountering three types 
of patterns: firstly, the southern pattern where the building 
mass is connected to the alleyway (A4, A5, A6); secondly, the 
northern pattern where the courtyard lies between the 
alleyway and the mass (B1, B2, B3); and thirdly, the pattern 
situated between two alleyways (A1, A2, A3) (Figure 2). To 
build the base model, the most common patterns of plans with 
areas ranging from 70 to 80 square meters and 130 to 140 
square meters were obtained [59, 60]. Based on the process 
undertaken, 9 patterns were selected for simulation. It is 
worth mentioning that the architectural plan of patterns with 
alleys on both sides (A1, A2, A3) was assumed to be shared 
with southern patterns (A4, A5, A6). 

 

 
 
Figure 2. Site plans of the basic patterns 

4. Results and discussion 

In this section, following the evaluation framework of the 
Green Building Index (GBI) [61, 62], the model is prepared for 
metamodel development by reducing design parameters. 
During this preparation phase, the design variables are 
narrowed down to those directly influencing energy 
performance, particularly in the early stages of the design 
process. In light of this, different engineering Software and 
BIM Technology tools can efficiently address these variables 
in the early stages of the design process for interior spaces 
and even redesign of major urban areas [63-65]. The 
simplified model, which incorporates only key design 
parameters, is aligned with relevant codes and regulations to 
ensure both efficiency and compliance. 

4.1 Architectural design parameters  
Architectural design parameters are recognized as key 

determinants in the early stages of the design process, 
particularly in shaping common architectural patterns.  

 

 

 

 
A questionnaire was used to identify the parameters directly 
used by architects during the initial stages of the design 
process. The questionnaire allowed respondents to select 
more than one option. The sampling method was purposive, 
targeting experienced architects capable of providing 
relevant insights in this field. Architects with more than five 
years of professional experience were selected. A preliminary 
face-to-face pre-test was conducted with five architects to 
assess the validity and reliability of the questionnaire, leading 
to initial modifications to enhance clarity. Subsequently, the 
revised questionnaire was distributed electronically to the 
target population, yielding 50 responses. Of these, 48 were 
deemed valid, while two questionnaires were excluded due to 
more than 25% of their items being left unanswered. To 
evaluate the significance of each parameter, the average 
response value was used. Parameters with response scores 
above the population’s overall average were considered 
significant and retained for analysis. These performance 
metrics (Table 2 and Table 3) were selected based on the 
reliable building codes. 

4.2 Developing the metamodel  
The first step in this section is to define the main 

constraints for building the metamodel. Limiting the inputs is 
crucial, which is obtained through interviews with architects 
and based on the research objectives. However, many input 
data may not be available in the early stages; therefore, it is 
necessary to use default values and patterns as constant 
parameters to save time and prevent potential errors. Typical 
specifications for all patterns in terms of partitioning, form, 
and details are provided in Tables 4 to 6.  

4.3 Sensitivity analysis of design parameters  
This research adopted the variance-based (Sobol) 

method to explore the interactions among design parameters. 
The goal of Sobol sensitivity analysis is to demonstrate the 
contribution of each input factor to the total output variance 
of the model and its interactions with other inputs [37, 42, 
47]. First-order and total effects are key indices used in this 
approach. The first-order index indicates the share of the 
main effect of each input variable on the output variance and 
is suitable for prioritization. If the values of the Sobol indices 
are greater than 0.10, the parameter is very sensitive; if they 
range from 0.01 to 0.10, the parameter is sensitive, and if they 
are less than 0.01, the parameter is not sensitive. It can be 
argued that if the primary goal is to prioritize energy-saving 
measures, first-order effects are a good option. Conversely, if 
the main goal is to identify factors that are not significant in 
energy models, total effects should be used. 

4.4 Sampling methodology  
The method for selecting samples for sensitivity analysis 

is as follows: Energy loads of the nine patterns constructed as 
the metamodel were calculated with energy-plus. Then, the 
three patterns with the highest, lowest, and median energy 
consumption were selected for sensitivity analysis. This 
selection was made to explore the maximum depth of design 
space.  

 

Zone Code 
Subzone General 

Specifications 

Maximum 
allowed 
density 

Maximum 
number of floors 

Maximum floor 
area (FAR) 

Minimum parcel size 
(square meters) 

Minimum width of 
the alley (meters) 

R122 Residential 300 5 60 250 10 
 



MF. Al-Kazee et al. /Future Technology                                                                                          May 2025| Volume 04 | Issue 02 | Pages 30-40 

34 

 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Table 2. Design parameters related to building structure in the initial stages of the design process and their values based on regulations 

Design Parameters Related to Building Structure 
Range 

Unit 
Minimum Maximum 

A
) P

lan
 

Skylight area 12 24 Square Meter 

- Dividers Properties (light reflectance 
coefficient) 

Floor 20 60 Percentage 
Ceiling 40 90 Percentage 

Wall 40 80 Percentage 
- Dimensions and geometric proportions 

(Aspect ratio) 
x-axis fix fix fix 
y-axis 0/50 0/80 Percentage 

- Floor plan depth relative to window North 4/5 7 Meter 
South 4/5 7 Meter 

B
) B

u
ild

in
g F

o
rm

 

- Terrace x-axis 1 12 Meter 

y-axis 1 2 Meter 

- Floor height  2/4 3 Meter 

- Relative compactness (Volume-to-surface ratio) 1/5 3 - 

- Form factor (Surface area to conditioned space ratio) 1 2 - 

C
) B

u
ild

in
g E

n
v

elo
p

e
 

- Surface color and absorption coefficient 10 100 Percentage 

- Window-to-Wall Ratio (WWR) - 
Transparent Surfaces 

North 10 90 Percentage 
South 10 90 Percentage 

- Shape and Geometry (Width and Height) 
- Transparent Surfaces 

North x-axis 0/50 3/0 Meter 
y-axis 0/50 3/0 Meter 

South x-axis 0/50 3/0 Meter 
y-axis 0/50 3/0 Meter 

- Surface Color and Absorption Coefficient - Opaque Surfaces 10 90 Percentage 

 

 

Table 3. Design parameters related to site design in the initial stages of the design process and their values according to regulations 

Design Parameters Related to Site Design 
Range Unit 

Minimum Maximum  

 
Site Design 

- Direction 0 180 Degree 

- Shading 0 50 Percentage 
- Sky exposure factor 10 25 Degree 

- Floor area fix fix 60 percent 

 

 

Table 4. Common specifications of patterns in terms of partitioning 

Ground Floor 
Number of 

Floors 
Building Width (meters) Width of alleyway (meters) Area of Parcells (square meters) 

Pilot 5 12 10 300 

 

 

Table 5. Common specifications of patterns in terms of default implementation assumptions 

Structure Type Interior walls Exterior walls Ceilings of the floors Window type 

 Material Thickness Material Thickness Material Height Type Frame Material 

Concrete Plaster 10 cm Clay brick 20 cm Beam 40 cm Two shells Aluminum 

 

 

Table 6. Common specifications of patterns in terms of mechanical system and space occupancy time 

Space Usage 
HVAC System 

Space Usage Time 
Cooling Heating 

Residential 27 23 Permanent 

 



MF. Al-Kazee et al. /Future Technology                                                                                          May 2025| Volume 04 | Issue 02 | Pages 30-40 

35 

 

Based on the simulation results, pattern A1 was chosen 
as the least energy-consuming pattern, A5 as the most energy-
consuming, and A6 as the median pattern for sensitivity 
analysis (Table 1 and Table 2). Here, several variables, based 
on their position in the initial stages of the design process 
according to the questionnaire, were not considered for the 
following reasons: 
Skylight area: Since the basis for selecting patterns for 
sensitivity analysis was the energy load, the selected patterns 
did not have any skylight areas. 
Relative compactness and form factor variables: These 
two variables are dependent on the width, length, and height 
of the building. Since the detailed plan requires buildings to 
adhere to a 60% occupancy pattern, the width and length of 
the building are always constant, and these two variables 
depend on the floor height. Considering that the height of the 
floor is examined in the sensitivity analysis parameters, these 
two parameters are indirectly investigated. 
Plan depth examination: Due to the shallow depth of the 
space in the case study (7 meters) and the fact that according 
to chapter 19th of the national regulations, compliance with a 
depth of up to 7 meters is allowed. In fact, even in the worst-
case scenario, the building would be adequately lit naturally 
(simulation results also supported this point). It is worth 
mentioning that according to the simulation results, patterns 
A1, A2, B1, A3, A5, A4, B3, B2, and A6 are the least energy-
consuming patterns, respectively. 

4.5 Comparative analysis of the scenarios  
Sensitivity analysis was conducted with the aim of 

prioritizing and assessing the interactive effects of 
parameters for the three selected patterns. The sensitivity 
analysis of design variables for the heating function in 
patterns shows that in all three patterns, the floor height has 
the greatest impact on heating. The absorption coefficient of 
the internal wall is the next most influential variable in all 
three patterns. NWWR, SWWR, and building orientation rank 
next in terms of their impact on heating in all three patterns. 
The sensitivity analysis of design variables for the cooling 
function in patterns A1, A5, and A6 indicates that in pattern 
A1, identified as the least energy-consuming, and A5, 
considered the mid-rise pattern in terms of energy 
consumption, NWWR has the most significant impact on 
cooling, followed by SWWR. In pattern A6, recognized as the 
most energy-consuming, SWWR has the highest influence on 
cooling, followed by NWWR. In all three patterns, after 
NWWR and SWWR, the absorption coefficient of the internal 
wall is the following influential parameter. Following these 
three parameters, the emissivity coefficient of external wall 
materials and the depth of the balcony rank next in terms of 
their impact on cooling. While floor height had the greatest 
impact on the heating function, it has less significance in the 
cooling function alongside parameters such as floor and wall 
absorption coefficients and shadow wall. The sensitivity 
analysis of design variables for the lighting function in 
patterns A1, A5, and A6 indicates that the absorption 
coefficient of room materials has the most significant impact 
compared to other parameters. Following this parameter, 
NWWR, SWWR, and the building orientation are in the next 
ranks. Each of these five parameters exhibits mutual and 
nonlinear effects on each other. This suggests that room 
lighting can be more influenced by the color compared to the 
window area. Floor height, balcony depth, and shadow have 
minimal effects on lighting compared to other parameters. 
The results also indicate that the emissivity coefficient of the 

external wall has no significant impact on the interior lighting 
function. 

Table 7 presents the first-order sensitivity index values, 
illustrating the influence of each variable on the objective 
functions (outputs). The roof absorptance coefficient exhibits 
the greatest impact on illumination, while the northern 
window-to-wall ratio (WWR) most significantly affects 
cooling. Floor height emerges as the most influential factor in 
heating. Among the ten variables analyzed, both the southern 
WWR and the internal wall absorptance coefficient show a 
consistently positive effect on all three objective functions 
across all three design patterns. The northern WWR 
consistently has the highest influence on cooling across all 
patterns and also demonstrates a substantial positive effect 
on illumination. However, its influence on heating is negative 
in patterns A1 and A5, whereas it becomes positive in pattern 
A6. This suggests that the northern window contributes more 
significantly to cooling and daylighting than to heating. 
Although southern exposure generally provides more 
illumination than northern exposure, a reduced southern 
WWR tends to optimize both illumination and heating 
performance. The unique behavior observed in pattern A6, 
where the northern WWR positively impacts heating, is 
attributed to the architectural configuration: one residential 
unit is exposed exclusively to northern light, while the other 
receives only southern light.  

The diffusion coefficient of the outer wall demonstrates 
a direct influence on both heating and cooling across all three 
design patterns, while it has no observable effect on 
illumination in any of them. In this study, this is the only 
variable that showed no impact on one of the objective 
functions. The shading device depth exhibits a minimal and 
negligible effect in all three patterns, contributing slightly 
negatively to both heating and cooling. Similarly, the depth of 
the terrace has a minimal influence, showing a slight positive 
effect on heating and cooling and a minor negative impact on 
illumination. 

Among these variables, building orientation in pattern 
A6 shows a positive influence on both heating and cooling 
performance. In contrast, in patterns A1 and A5, orientation 
has a slightly negative, though negligible, effect on these 
functions. As such, careful consideration of orientation is 
recommended. On the other hand, the orientation variable 
positively affects illumination in all three patterns, which may 
be attributed to the relatively shallow depth of the floor plans, 
allowing greater daylight penetration. 

In conclusion, the absorption coefficient of the internal 
wall for all three objective functions, the north and south 
window-to-wall ratios (WWR) for cooling and illumination, 
the floor height for heating, and the ceiling and floor 
absorption coefficients for illumination are classified within 
the highly sensitive group. Conversely, the diffusion 
coefficient of the outer wall for heating and cooling, the south 
WWR for heating, and the balcony for heating and cooling fall 
within the sensitive group. 

The case studies illustrate the variations in objective 
functions (outputs) based on each variable and their 
interrelationships within patterns A1, A5, and A6. For the 
heating function across all three patterns, floor height has the 
most direct impact. The variables NWWR and building 
orientation, which exert a small negative direct effect on 
heating, have the most interactive effects on other variables 
after floor height in the heating function. For the cooling 
function in patterns A1 and A5, NWWR and the absorption 
coefficient of the internal wall have the most interactive 
effects on other variables, followed by SWWR.  



MF. Al-Kazee et al. /Future Technology                                                                                          May 2025| Volume 04 | Issue 02 | Pages 30-40 

36 

 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
In pattern A6, NWWR and SWWR have the greatest 

interactive effects on other variables for cooling. For the 
illumination function in pattern A1, NWWR has the most 
interactive effect on other variables, followed by the 
absorption coefficient of the floor, SWWR, the absorption 
coefficient of the ceiling, and building orientation. In pattern 
A5, the absorption coefficient of the floor has the most 
interactive effect on other variables, followed by NWWR, 
SWWR, the absorption coefficient of the ceiling, and building 
orientation. Finally, in pattern A6, the absorption coefficient 
of the floor has the most interactive effect on other variables, 
followed by the absorption coefficient of the ceiling, SWWR, 
building orientation, and NWWR. The primary results of the 
research were derived from the total order index, which 
assesses the impact of each variable and its interactions with 
other variables on the objective functions in patterns A1, A5, 
and A6. According to Table 8, the variables of northern WWR 
and building orientation, which had a minor impact on 
heating in the first-order sensitivity index and were classified 
as non-sensitive in all three patterns, emerge as significant 
factors affecting building heating in the total-order sensitivity 
index due to their interactions with other variables.  

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
The northern WWR is classified in the very sensitive 

group, while building orientation falls in the sensitive group. 
In the illumination function (Table 7), the building orientation 
variable, which showed a minor effect in the first-order 
sensitivity index and was classified as non-sensitive, is 
identified as an influential factor in building lighting in the 
total-order sensitivity index, placing it in the very sensitive 
group. For illumination, although the northern WWR ranked 
second in terms of impact in the first-order index, it ranks first 
in terms of impact in the total-order index. Based on 
simulation results, sensitivity indices for both first-order and 
total-order sensitivity in the cooling function show no 
significant differences, except for the plan depth parameter, 
which changes from a minimal positive impact to a minimal 
negative impact. For the heating function, in the first-order 
sensitivity index, six parameters are important: southern 
WWR, height, internal wall absorption coefficient, ceiling 
absorption coefficient, balcony, and shading. In the total-
order sensitivity index, which accounts for parameter 
interactions, two variables- northern WWR and building 
orientation- are added compared to the first-order index, 
while southern WWR is removed. For the cooling function, in 

Table 7. First-order sensitivity index values of variables on the tri-objective functions 

A6 A5 A1 Pattern   

Il
lu

m
in

at
io

n
 

C
o

o
li

n
g 

H
ea

ti
n

g 

Il
lu

m
in

at
io

n
 

C
o

o
li

n
g 

H
ea

ti
n

g 

Il
lu

m
in

at
io

n
 

C
o

o
li

n
g 

H
ea

ti
n

g 

 
                  Variable 
 
Objective 
 Function 

0.0489 0.0492 0.0781 0.0016 -0.0085 -0.045 0.0076 -0.0077 -0.0408 orientation 
0.0124 -0.0008 0.2346 0.0101 -0.0027 0.3316 0.0075 -0.0096 0.3186 Floor height 
0.1702 0.0831 0.1677 0.1551 0.3296 0.2850 0.1303 0.2604 0.2544 WSA 
0.2751 -0.0232 -0.0317 0.3849 -0.0234 -0.0213 0.3481 -0.0228 -0.0201 FSA 
0.1551 0.3571 0.0151 0.2452 0.4629 -0.0312 0.3161 0.5049 -0.028 NWWR 
-0.0012 0.0028 -0.0019 0.0046 0.0026 0.0031 -0.0043 0.0042 0.0057 terrace depth 
0.3230 0.4502 0.0865 0.2453 0.1381 0.03 0.2614 0.1931 0.0326 SWWR 

0 0.400 0.0367 0 0.0631 0.1435 0 0.0588 0.0214 WTA 
0.1517 -0.0133 -0.0185 0.1702 -0.0068 -0.0125 0.1649 -0.0108 -0.016 CSA 
-0.0007 -0.0027 -0.0101 4.43e-05 -0.022 -0.0205 0.0011 -0.0188 -0.0214 Overhang depth 

 

Table 8. Total-order sensitivity index, indicating the influence of variables on the objective functions using the Sobol method 

A6 A5 A1 Pattern 

Il
lu

m
in

at
io

n
 

C
o

o
li

n
g 

H
ea

ti
n

g 

Il
lu

m
in

at
io

n
 

C
o

o
li

n
g 

H
ea

ti
n

g 

Il
lu

m
in

at
io

n
 

C
o

o
li

n
g 

H
ea

ti
n

g 

 
                  Variable 

 
Objective   
Functions 

0.1825 0.0294 0.2136 0.0553 -0.0182 0.0734 0.0735 -0.0138 0.0864 Orientation 

0.0215 -0.0052 0.2304 0.0170 -0.0246 0.3322 0.0088 -0.0275 0.3194 Floor height 

0.1862 0.0972 0.1789 0.0512 0.3606 0.2823 0.0417 0.2870 0.2573 WSA 

0.3279 -0.0406 -0.0242 0.3659 -0.0348 -0.0115 0.3019 -0.0304 -0.0109 FSA 

0.1881 0.3563 0.2167 0.2847 0.4441 0.1865 0.3594 0.4886 0.1997 NWWR 

0.0088 0.0085 -0.020 -0.0019 -0.0068 0.0043 -0.0083 -0.0059 0.0035 terrace depth 

0.3610 0.4276 0.09320 0.2500 0.1414 -0.0017 0.2607 0.2017 0.0038 SWWR 

0 0.0407 0.0220 0 0.0708 -0.0019 5.5e-017 0.0659 0.0041 WTA 

0.2166 -0.0231 -0.0119 0.2151 -0.0162 -0.0055 0.1886 -0.0165 -0.0065 CSA 

0.0003 -0.0017 -0.0072 -0.0011 -0.0214 -0.0256 0.0013 -0.0167 -0.0245 Overhang depth 

 



MF. Al-Kazee et al. /Future Technology                                                                                          May 2025| Volume 04 | Issue 02 | Pages 30-40 

37 

 

the first-order sensitivity index, seven parameters are 
important: southern and northern WWR, internal wall 
absorption coefficient, ceiling absorption coefficient, balcony, 
shading, and external wall diffusion coefficient. In the total-
order sensitivity index, the shading variable is removed 
compared to the first-order index. For the illumination 
function, in the first-order sensitivity index, seven parameters 
are influential: floor absorption coefficient, southern and 
northern WWR, ceiling absorption coefficient, internal wall 
absorption coefficient, orientation, and balcony. In the total-
order sensitivity index, which considers the interaction of 
parameters, building orientation has a greater impact 
compared to the first-order index. 

5. Conclusion  

The conclusions of this study are based on a sensitivity 
analysis of design parameters influencing three key objective 
functions: heating, cooling, and illumination. The analysis 
focused on variables such as building orientation, WWR, 
shading depth, internal surface absorption coefficients (walls, 
floors, and ceilings), and the albedo of external wall surfaces. 
• Northern WWR: This parameter has a significant influence 

on cooling and also contributes to daylight performance. 
Although its direct impact on heating is limited, its 
interactions with other variables—captured through the 
Sobol sensitivity analysis—make it a key parameter, 
accounting for approximately 20% of the total variance 
among the ten variables studied. 

• Southern WWR: This parameter affects all three objective 
functions. It exhibits a strong direct effect on both 
illumination and cooling, and its interactions with other 
parameters are also substantial. However, its interactive 
effect on heating is less pronounced, with its contribution 
to heating primarily driven by direct influence. 

• Floor Height: This variable shows the highest sensitivity 
with respect to heating, while its influence on cooling and 
illumination is minimal. Sensitivity analysis of its 
interaction with other parameters indicates that it does not 
play a significant role in cooling and illumination functions. 

• Building Orientation: Orientation has a considerable direct 
impact on daylight performance, both independently and 
through interaction with other parameters. While it shows 
little direct effect on heating, its interactive role with other 
influential variables enhances its significance in heating-
related outcomes. 

• Internal Wall Absorption Coefficient: This parameter 
directly affects all three objective functions. Its influence on 
heating and cooling is nearly equal and more substantial 
than its effect on illumination. It also shows notable 
interactive effects across all three functions. 

• External Wall Diffusion Coefficient (Albedo): This variable 
significantly impacts both heating and cooling, with no 
measurable influence on illumination. Its effects are 
primarily manifested through direct contributions and 
interactions with thermal parameters. 

• Shading Depth: Although this parameter has some level of 
influence on heating, cooling, and illumination across all 
three design patterns, its overall sensitivity is relatively low 
compared to the other design parameters. 

The sensitivity analysis of design parameters for the objective 
functions of heating, cooling, and illumination in BSk 
residential typologies- specifically patterns A1, A5, and A6-
demonstrates a high level of consistency in how key variables 
influence energy performance. This consistency supports the 
feasibility of developing a standardized reference database 
that captures the relationships and interdependencies among 

critical design parameters. Furthermore, the findings 
highlight that architects’ preferences and the prioritization of 
design parameters significantly affect energy efficiency 
during the early stages of the design process. The results 
underscore the importance of the model preparation and 
meta-model construction phases, both of which play a crucial 
role in enabling informed, performance-based design 
decisions. This framework enables designers to monitor key 
variables and assess the sensitivity of their design decisions, 
ultimately informing strategies for optimal energy 
performance. From an initial pool of 50 energy-relevant 
design parameters, 10 were selected- based on expert input 
for sensitivity analysis. These include building orientation; 
northern and southern window-to-wall ratio (WWR); shading 
depth; internal wall, roof, and floor absorption coefficients; 
external wall diffusion coefficient (albedo); floor height; and 
balcony depth. The findings reveal that the northern and 
southern WWR and internal wall absorption coefficient exert 
the greatest influence on heating, cooling, and daylighting 
outcomes. Additionally, the simulation results from three of 
nine representative mid-rise residential patterns suggest the 
feasibility of developing a reference database to provide 
effective, evidence-based design guidance. The designer 
approach toward water-energy efficiency allows for the 
quantification of both direct and interactive effects of urban 
regulations on building energy performance in a dense 
metropolitan context. Through sensitivity analysis, 
prescriptive and practical design priorities can be 
established. Given that the framing approach aligns well with 
the iterative and decision-intensive nature of architectural 
design, it offers a valuable structure for further development-
especially within the context of participatory design 
frameworks. Therefore, this conclusion represents a 
meaningful step toward advancing the concept of high 
performance architecture, including initiatives such as 
“Zukunft Bau” and “Büro von Morgen”, which are dedicated to 
mitigating climate change through water- and energy-
sensitive architectural design processes.  

Ethical issue 
The authors are aware of and comply with best practices in 
publication ethics, specifically with regard to authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with policies on research ethics. The authors adhere to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

Data availability statement 
The manuscript contains all the data. However, more data will 

be available upon request from the authors. 

Conflict of interest 

The authors declare no potential conflict of interest. 

References 

[1] Nahan, R.T., 2019. Architect’s Guide to Building 

Performance. Integrating Performance Simulation in 

the Design Process. Available: American Institute of 

Architects.  Available: https://www.aia.org/resource-

center/architects-guide-building-performance 

[2]  Akbari, M., Souhankar, A. and Heidari, H., 2021. The 

Roadmap to Establish the Ministry of Energy in Iran. 

Rahyaft, 31(2), pp.87-104. 

https://doi.org/10.22034/rahyaft.2021.10884.1267 



MF. Al-Kazee et al. /Future Technology                                                                                          May 2025| Volume 04 | Issue 02 | Pages 30-40 

38 

 

[3]  Eslamirad, N., Malekpour Kolbadinejad, S., 

Mahdavinejad, M. and Mehranrad, M., 2020. Thermal 

comfort prediction by applying supervised machine 

learning in green sidewalks of Tehran. Smart and 

Sustainable Built Environment, 9(4), pp.361-374. 

https://doi.org/10.1108/SASBE-03-2019-0028 

[4]  Hakiminejad, A., Fu, C. and Mohammadzadeh 

Titkanlou, H., 2015, June. A critical review of 

sustainable built environment development in Iran. In 

Proceedings of the Institution of Civil Engineers-

Engineering Sustainability (Vol. 168, No. 3, pp. 105-

119). Thomas Telford Ltd. Available: 

https://www.icevirtuallibrary.com/doi/full/10.1680/

ensu.14.00017 

[5]  Khalaji Assadi, M., 2013. Energy demand model of the 

household sector and its application in developing 

metropolitan cities (case study: Tehran). Pol. J. 

Environ. Stud, 22(2), pp.319-329. 

http://scholars.utp.edu.my/id/eprint/11661/1/7Ener

gydemandmodelofhouseholdsector.pdf 

[6]  Eslamirad, N. and Mahdavinejad, M., 2018, June. Multi 

objective computing and applying expert system in 

Double Skin Façade system. In Proceedings of the 

Ninth International Conference on Future Energy 

Systems (pp. 459-461). 

https://doi.org/10.1145/3208903.3212060 

[7]  Altavilla, F., Vicari, B., Hensen, J.L.M. and Filippi, M., 

2004. Simulation tools for building energy design. In 

conference; PhD symposium Modelling and Simulation 

for Environmental Engineering; 2004-04-16; 2004-04-

16 (pp. 39-46). 

https://research.tue.nl/en/publications/simulation-

tools-for-building-energy-design  

[8]  Anderson, K., 2014. Design energy simulation for 

architects: Guide to 3D graphics. Routledge. 

https://doi.org/10.4324/9781315851433  

Anderson, K., 2014. Design energy simulation for 

architects: Guide to 3D graphics. Routledge. 

[9]  Attia, S., Gratia, E., De Herde, A. and Hensen, J.L., 2012. 

Simulation-based decision support tool for early stages 

of zero-energy building design. Energy and buildings, 

49, pp.2-15. 

https://doi.org/10.1016/j.enbuild.2012.01.028 

[10]  Barton, R.R. and Meckesheimer, M., 2006. Metamodel-

based simulation optimization. Handbooks in 

operations research and management science, 13, 

pp.535-574. https://doi.org/10.1016/S0927-

0507(06)13018-2 

[11]  Bragança, L., Vieira, S.M. and Andrade, J.B., 2014. Early-

stage design decisions: the way to achieve sustainable 

buildings at lower costs. The scientific world journal, 

2014(1), p.365364. 

https://doi.org/10.1155/2014/365364 

[12]  Choi, S.Y. and Kim, S.H., 2021. Knowledge acquisition 

and representation for high-performance building 

design: A review for defining requirements for 

developing a design expert system. Sustainability, 

13(9), p.4640. https://doi.org/10.3390/su13094640 

[13]  De Boeck, L., Verbeke, S., Audenaert, A. and De 

Mesmaeker, L., 2015. Improving the energy 

performance of residential buildings: A literature 

review. Renewable and Sustainable Energy Reviews, 

52, pp.960-975. 

https://doi.org/10.1016/j.rser.2015.07.037 

[14]  Delgarm, N., Sajadi, B., Azarbad, K. and Delgarm, S., 

2018. Sensitivity analysis of building energy 

performance: A simulation-based approach using 

OFAT and variance-based sensitivity analysis methods. 

Journal of Building Engineering, 15, pp.181-193. 

https://doi.org/10.1016/j.jobe.2017.11.020 

[15]  Van Dooren, E., Boshuizen, E., Van Merriënboer, J., 

Asselbergs, T. and Van Dorst, M., 2014. Making explicit 

in design education: Generic elements in the design 

process. International Journal of Technology and 

Design Education, 24, pp.53-71. 

https://doi.org/10.1007/s10798-013-9246-8 

[16]  Dorst, K., 2011. The core of ‘design thinking’ and its 

application. Design studies, 32(6), pp.521-532. 

https://doi.org/10.1016/j.destud.2011.07.006 

[17]  Goharian, A., Daneshjoo, K., Shaeri, J., Mahdavinejad, M. 

and Yeganeh, M., 2023. A designerly approach to 

daylight efficiency of central light-well; combining 

manual with NSGA-II algorithm optimization. Energy, 

276, p.127402. 

https://doi.org/10.1016/j.energy.2023.127402  

[18]  Feria, M. and Amado, M., 2019. Architectural design: 

Sustainability in the decision-making process. 

Buildings, 9(5), p.135. 

https://doi.org/10.3390/buildings9050135 

[19]  Fernandez-Antolin, M.M., del Río, J.M. and González-

Lezcano, R.A., 2022. Building Performance Simulations 

and Architects against Climate Change and Energy 

Resource Scarcity. Earth, 3(1), pp.31-44. 

https://doi.org/10.3390/earth3010003 

[20]  Lemke, C.E., 1985. Introduction to Sensitivity and 

Stability Analysis in Nonlinear Programming (Anthony 

V. Fiacco). SIAM Review, 27(1), p.114. 

https://doi.org/10.1137/1027038  

[21]  Hemsath, T.L. and Bandhosseini, K.A., 2015. Sensitivity 

analysis evaluating basic building geometry's effect on 

energy use. Renewable Energy, 76, pp.526-538. 

https://doi.org/10.1016/j.renene.2014.11.044  

[22]  Hester, J., Gregory, J. and Kirchain, R., 2017. Sequential 

early-design guidance for residential single-family 

buildings using a probabilistic metamodel of energy 

consumption. Energy and Buildings, 134, pp.202-211. 

https://doi.org/10.1016/j.enbuild.2016.10.047 

[23]  Hiyama, K., 2015. Assigning robust default values in 

building performance simulation software for 

improved decision-making in the initial stages of 

building design. The Scientific World Journal, 2015(1), 

p.246718. https://doi.org/10.1155/2015/246718 

[24]  Ianni, M. and Sánchez de León, M., 2013. Applying 

Energy Performance-Based Design in Early Design 

Stages. A methodological framework for integrating 

multiple BPS tools. Available: 

http://resolver.tudelft.nl/uuid:6f0bb5d1-e0ef-4ae7-

956e-70bdd4ccf753 

[25]  Gholami, H., Kamelnia, H., Mahdavinejad, M.J. and 

Sangin, H., 2025. Optimizing Building Configuration 

and Orientation for Social Housing Projects in Iran. 

Iranica Journal of Energy & Environment, 16(2), 



MF. Al-Kazee et al. /Future Technology                                                                                          May 2025| Volume 04 | Issue 02 | Pages 30-40 

39 

 

pp.289-308. 

https://doi.org/10.5829/ijee.2025.16.02.11  

[26]  Kistelegdi, I., Horváth, K.R., Storcz, T. and Ercsey, Z., 

2022. Building Geometry as a Variable in Energy, 

Comfort, and Environmental Design Optimization—A 

Review from the Perspective of Architects. Buildings, 

12(1), p.69. 

https://doi.org/10.3390/buildings12010069 

[27]  Lawson, B., 2006. How designers think: The design 

process demystified. Routledge. 

https://doi.org/10.4324/9780080454979 

[28]  Goodarzi, P., Heidari, F., Zolotovsky, K. and 

Mahdavinejad, M., 2025. Cognitive Nests: Nested Data-

Driven Decision Support System in Regenerative 

Design from Biology to Ecology. 3D Printing and 

Additive Manufacturing. 

https://doi.org/10.1089/3dp.2023.0331 

[29]  Hosseini, S. A., Fathi, S., Bemanian, M., Mahdavinejad, 

M. (2025). 'Decoding Architectural Design Elements 

Through Shape Grammar (Typology of 64 traditional 

houses of Kashan, Yazd, and Isfahan)', The Monthly 

Scientific Journal of Bagh-e Nazar, 21(141), pp. 5-14. 

https://doi.org/10.22034/bagh.2025.424282.5497 

[30]  Markelj, J., Kitek Kuzman, M., Grošelj, P. and Zbašnik-

Senegačnik, M., 2014. A simplified method for 

evaluating building sustainability in the early design 

phase for architects. Sustainability, 6(12), pp.8775-

8795. https://doi.org/10.3390/su6128775 

[31]  McKeen, P. and Fung, A.S., 2014. The effect of building 

aspect ratio on energy efficiency: A case study for 

multi-unit residential buildings in Canada. Buildings, 

4(3), pp.336-354. 

https://doi.org/10.3390/buildings4030336 

[32]  Mahdavinejad, M., Gharaati, F., and Galil M.I., 2025. 

Anticipating the Future of Iranian Cities: High-Tech 

Versus Nature-Based Solutions. Cities as Anticipatory 

Systems, p.103. https://doi.org/10.1007/978-3-031-

78162-9_7  

[33]  Nguyen, A.T., Reiter, S. and Rigo, P., 2014. A review on 

simulation-based optimization methods applied to 

building performance analysis. Applied energy, 113, 

pp.1043-1058. 

https://doi.org/10.1016/j.apenergy.2013.08.061 

[34]  Dezfuli, R.R., Mehrakizadeh, M., Najar, B.S.A., 

Bazazzadeh, H. and Mahdavinejad, M., 2024. Geometric 

investigation of entrance proportions of houses from 

the Qajar to the beginning of the early Pahlavi in Dezful 

City (1789–1979). Frontiers of Architectural Research, 

13(1), pp.57-78. 

https://doi.org/10.1016/j.foar.2023.09.007  

[35]  Heidari, F., Mahdavinejad, M., Zolotovsky, K. and 

Bemanian, M., 2024. Data-driven bio-integrated design 

method encoded by biocomputational real-time 

feedback loop and deep semi-supervised learning 

(DSSL). Journal of Building Engineering, 98, p.110923. 

https://doi.org/10.1016/j.jobe.2024.110923  

[36]  Purup, P.B. and Petersen, S., 2020. Requirement 

analysis for building performance simulation tools 

conformed to fit design practice. Automation in 

Construction, 116, p.103226. 

https://doi.org/10.1016/j.autcon.2020.103226 

[37]  Rodríguez, G.C., Andrés, A.C., Muñoz, F.D., López, J.M.C. 

and Zhang, Y., 2013. Uncertainties and sensitivity 

analysis in building energy simulation using 

macroparameters. Energy and Buildings, 67, pp.79-87. 

https://doi.org/10.1016/j.enbuild.2013.08.009 

[38]  Mahdavinejad, M., Bazazzadeh, H., Mehrvarz, F., 

Berardi, U., Nasr, T., Pourbagher, S. and Hoseinzadeh, 

S., The impact of facade geometry on visual comfort 

and energy consumption in an office building in 

different climates. Energy Reports 11 (2024) 1–17 

[online] https://doi.org/10.1016/j.egyr.2023.11.021  

[39]  Sadineni, S.B., Madala, S. and Boehm, R.F., 2011. 

Passive building energy savings: A review of building 

envelope components. Renewable and sustainable 

energy reviews, 15(8), pp.3617-3631. 

https://doi.org/10.1016/j.rser.2011.07.014 

[40]  Mahdavinejad M, Shaeri J, Nezami A, Goharian A. 

Comparing universal thermal climate index (UTCI) 

with selected thermal indices to evaluate outdoor 

thermal comfort in traditional courtyards with BWh 

climate. Urban Climate. 2024 Mar 1; 54:101839. 

https://doi.org/10.1016/j.uclim.2024.101839 

[41]  Schon, D.A., 2010. Educating the reflective practitioner: 

Toward a new design for teaching and learning in the 

professions. Australian Journal of Adult Learning, 

50(2), pp.448-451. Available: https://ala.asn.au/wp-

content/uploads/ajal/2010/JournalJuly2010.pdf#pag

e=240 

[42]  Mehrvarz, F., Bemanian, M., Nasr, T., Mansoori, R., AL-

Kazee, M.F., Khudhayer, W.A. and Mahdavinejad, M., 

2024. Designerly Approach to Design Responsive 

Façade for Occupant Visual Comfort in Different 

Latitudes. Journal of Daylighting, 11(1), pp.149-164. 

https://doi.org/10.15627/jd.2024.9 Available: 

https://solarlits.com/jd/11-149 

[43]  Struck, C., de Wilde, P.J., Hopfe, C.J. and Hensen, J.L., 

2009. An investigation of the option space in 

conceptual building design for advanced building 

simulation. Advanced Engineering Informatics, 23(4), 

pp.386-395. 

https://doi.org/10.1016/j.aei.2009.06.004 

[44]  Tian, Z. and Lei, Y., 2019. Integrated Design and a 

Holistic Building Energy Performance Simulation 

Method for Staged Practices. In IOP Conference Series: 

Earth and Environmental Science (Vol. 218, No. 1, p. 

012064). IOP Publishing. 

https://doi.org/10.1088/1755-1315/218/1/012064 

[45]  Tupper, K. and Fluhrer, C., 2010, August. Energy 

modeling at each design phase: strategies to minimize 

design energy use. In SimBuild Conference 2010 (Vol. 

4, pp. 48-55). IBPSA-USA. 

https://publications.ibpsa.org/conference/paper/?id=

simbuild2010_TS02A-01-Tupper 

[46]  Vatani, M., Kiani, K., Mahdavinejad, M. and Georgescu, 

M., 2024. Evaluating the effects of different tree 

species on enhancing outdoor thermal comfort in a 

post-industrial landscape. Environmental Research 

Letters, 19(6), p.064051. 

https://doi.org/10.1088/1748-9326/ad49b7  

[47]  Zhao, S. and de Angelis, E., 2018. Performance-based 

generative architecture design: a review on design 



MF. Al-Kazee et al. /Future Technology                                                                                          May 2025| Volume 04 | Issue 02 | Pages 30-40 

40 

 

problem formulation and software utilization. Journal 

of Integrated Design and Process Science, 22(3), pp.55-

76. https://doi.org/10.3233/JID190001 

[48]  Zou, Y., Zhan, Q. and Xiang, K., 2021. A comprehensive 

method for optimizing the design of a regular 

architectural space to improve building performance. 

Energy Reports, 7, pp.981-996. 

https://doi.org/10.1016/j.egyr.2021.01.097 

[49]  Omrani Azizabad, S., Mahdavinejad, M. and Hadighi, M., 

2024. Three-dimensional embodied visibility graph 

analysis: Investigating and analyzing values along an 

outdoor path. Environment and Planning B: Urban 

Analytics and City Science, p.23998083241303199. 

https://doi.org/10.1177/23998083241303199  

[50]  Rahbar, M., Mahdavinejad, M., Bemanian, M., Davaie 

Markazi, A.H. and Hovestadt, L., 2019. Generating 

synthetic space allocation probability layouts based on 

trained conditional-GANs. Applied Artificial 

Intelligence, 33(8), pp.689-705. 

https://doi.org/10.1080/08839514.2019.1592919 

[51]  Rahbar, M., Mahdavinejad, M., Markazi, A.H. and 

Bemanian, M., 2022. Architectural layout design 

through deep learning and agent-based modeling: A 

hybrid approach. Journal of Building Engineering, 47, 

p.103822. 

https://doi.org/10.1016/j.jobe.2021.103822  

[53]  Najafi, Q., Gorji-Mahlabani, Y., Goharian, A. and 

Mahdavinejad, M., 2023. A Novel Design-based 

Optimization Method for Building by Sensitivity 

Analysis. Journal of Solar Energy Research, 8(2), 

pp.1446-1458. Available: 

https://jser.ut.ac.ir/article_91774.html 

[54]  Damir, M., Oevermann, H., Meyer, M., Mahdavinejad, M. 

and Elmouelhi, H., 2024. Sites of Modern Industrial 

Heritage in Egypt and Iran: Local, National, and 

International Relevance for Conservation and Reuse. 

Docomomo Journal, (71), pp.4-12. 

https://doi.org/10.52200/docomomo.71.02  

[55]  Dezfuli, R.R., Bazazzadeh, H., Taban, M. and 

Mahdavinejad, M., 2023. Optimizing stack ventilation 

in low and medium-rise residential buildings in hot 

and semi-humid climate. Case Studies in Thermal 

Engineering, 52, p.103555. 

https://doi.org/10.1016/j.csite.2023.103555  

[56]  Lamsal, P., Adhikari, A., Aryal, V. and Aryal, B., 2025. A 

Review of an In-depth Interview on Urban Planning 

and Development. Molung Educational Frontier, 15, 

pp.278-307. 

https://doi.org/10.3126/mef.v15i01.74035 

[57]  Eskandari, H., Saedvandi, M. and Mahdavinejad, M., 

2017. The impact of Iwan as a traditional shading 

device on the building energy consumption. Buildings, 

8(1), p.3. https://doi.org/10.3390/buildings8010003 

[58]  Fallahtafti, R. and Mahdavinejad, M., 2021. Window 

geometry impact on a room's wind comfort. 

Engineering, Construction and Architectural 

Management, 28(9), pp.2381-2410. 

https://doi.org/10.1108/ECAM-01-2020-0075  

[59]  Goharian, A. and Mahdavinejad, M., 2020. A novel 

approach to multi-apertures and multi-aspects ratio 

light pipe. Journal of Daylighting, 7(2), pp.186-200. 

https://doi.org/10.15627/jd.2020.17  

[60]  Goharian, A., Mahdavinejad, M., Ghazazani, S., Hosseini, 

M., Zamani, Z., Yavari, H., Ghafarpoor, F. and Shoghi, F., 

2025. Designing Adaptability Strategy to a Novel 

Kinetic Adaptive Façade (NKAF); Toward a Pioneering 

Method in Dynamic-objects Daylight Simulation (Post-

Processing). Journal of Daylighting, 12, pp.69-90. 

https://dx.doi.org/10.15627/jd.2025.5  

[61]  Solla, M., Elmesh, A., Memon, Z.A., Ismail, L.H., Kazee, 

M.F.A., Latif, Q.B.A.I., Yusoff, N.I.M., Alosta, M. and 

Milad, A., 2022. Analysis of BIM-based digitising of 

Green Building Index (GBI): Assessment method. 

Buildings, 12(4), p.429. 

https://doi.org/10.3390/buildings12040429 

[62]  Al Dughaishi, H., Al Lawati, J., Alosta, M., Mahmood, S., 

Al-Kazee, M.F., Yusoff, N.I.M. and Milad, A., 2023. 

Analysis and Design of Lateral Framing Systems for 

Multi-Story Steel Buildings. Applied Mechanics, 4(2), 

pp.389-406. 

https://doi.org/10.3390/applmech4020022 

[63]  Mohamed Faisal Al-Kazee, Racha Ramhamdani (2022). 

The Adoption of BIM Technology in the Architectural 

Consultancy Firms in GCC Region. Civil Engineering 

and Architecture, 10(1), 280 - 288. DOI: 

10.13189/cea.2022.100124. 

[64]  Al kazee, M. (2019). Software Engineering and Its Role 

in the Study of Office Space. Journal of Architecture, 

Arts and Humanistic Science, 4(13), 373-386. doi: 

10.21608/mjaf.2018.20407 

[65]  M. F. Al-Kazee, R. A. Osman, K. S. Al-Munaijri and N. K. 

Al-Naabi, "Old al-seeb sector: A contemporary 

approach to re-design major Urban areas," 2nd Smart 

Cities Symposium (SCS 2019), Bahrain, Bahrain, 2019, 

pp. 1-6, doi: 10.1049/cp.2019.0235. 

 

  

This article is an open-access article distributed under the 

terms and conditions of the Creative Commons Attribution 

(CC BY) license 

(https://creativecommons.org/licenses/by/4.0/). 

https://creativecommons.org/licenses/by/4.0/

