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/