The building sector is a major contributor to resource consumption, energy use, and greenhouse gas emissions. Sustainable architecture offers a solution, leveraging Building Energy Modeling (BEM) for early-stage design optimization. This study explores the use of genetic algorithms for optimizing sustainable design strategies holistically. A comprehensive analysis and optimization model was developed using genetic algorithms to individually optimize various sustainable strategies. The optimized strategies were then applied to a pre-existing building in Kabul City, a region facing significant environmental challenges. To enhance accuracy, this study integrated energy simulations with Computational Fluid Dynamics (CFD). This research combines genetic algorithms with energy simulation and CFD analysis to optimize building design for a specific climate. Furthermore, it validates the optimized strategies through a real-world case study building. Optimizing the Window-to-Wall Ratio (WWR) and shading devices based on solar exposure significantly improved the building’s energy performance. South (S)-facing single windows and specific combinations of opposing and adjacent windows emerged as optimal configurations. The strategic optimization of building component materials led to substantial energy savings: a 58.6% reduction in window energy loss, 78.3% in wall loss, and 69.5% in roof loss. Additionally, the optimized pre-existing building achieved a 48.1% reduction in cooling demand, a 97.5% reduction in heating demand, and an overall energy reduction of 84.4%. Improved natural ventilation and controlled solar gain led to a 72.2% reduction in peak-month CO₂ emissions. While this study focused on applicable passive design strategies, the integration of advanced technologies like Phase Change Materials (PCMs), kinetic shading devices, and renewable energy systems can further improve building performance and contribute to achieving net-zero buildings.

The rising demand for building cooling has led to increased energy consumption and environmental concerns. This study aims to examine the effectiveness of natural ventilation strategies as sustainable approaches in arid and semi-arid climates, focusing on Kabul City. Employing integrated simulation techniques, including annual energy simulation and Computational Fluid Dynamics (CFD) analysis, various ventilation approaches were evaluated, such as single-side ventilation, cross-ventilation, and windcatchers. Results indicate that cross-ventilation with opposing windows significantly outperformed other strategies in terms of natural ventilation rates, achieving a 62.5% improvement over single-sided ventilation and a 36% improvement over cross-ventilation with adjacent windows. However, the complex interplay between solar gain and natural ventilation in these climates can lead to increased cooling demand. To address this challenge, integrating south-(S)-facing orientations into both single-sided and cross-ventilated configurations was found to be a balanced and effective approach. These scenarios demonstrated significant total energy savings. Among single-sided ventilation, the S-facing scenario achieved an average of 58.3% energy savings. For opposing cross-ventilation, the north-south (NS) scenario yielded 65.6% energy savings. Finally, adjacent cross-ventilation in the east-south (ES) and south-west (SW) scenarios resulted in an average of 51% energy savings. Furthermore, windcatchers, when combined with single-sided ventilation, further enhanced natural ventilation and reduced cooling demand while maintaining nearly consistent heating demand. Optimized rooftop windcatchers improved natural ventilation rates by up to 26.9% and attached windcatchers achieved up to 56.8% improvement. Compared to a baseline, rooftop windcatchers reduced cooling demand by 6.2%, and attached windcatchers by up to 18.7%. The findings of this study highlight the potential of window-based and windcatcher natural ventilation strategies as energy-efficient and balanced approaches for reducing building energy consumption, particularly cooling loads, in arid and semi-arid climates. These insights provide valuable guidance for architects and policymakers seeking to promote sustainable and energy-efficient building practices.

The proliferation of sustainable design approaches and assessment methods has resulted in a vast array of indicators. However, this abundance often leads to confusion during interpretation and application. Additionally, rapid urbanization and environmental concerns sometimes overshadow social and economic considerations, emphasizing environmental impact reduction. This study addresses these challenges through an integrated approach that combines a Systematic Literature Review (SLR) with a Decision-Making Trial and Evaluation Laboratory (DEMATEL) analysis to provide a holistic model for sustainable building design. The SLR was carried out individually through a relative Structural Query Language (SQL) regarding sustainable building design and vernacular principle. The output of SLR was subjected to DEMATEL model to recognize the holistic indicators interconnection and validate the proposed model. The research identified 23 global indicators for building sustainability worldwide, with five—Energy, Materials & Resources, Sites & Ecology, Indoor Environmental Quality, and Water—emerging as the most prevalent. Additionally, 22 consistently applied indicators in vernacular design practices exhibited significant overlap with those in sustainable design. This model integrated two novel indicators—Vernacular Principles and Social—Culture—with common sustainable building indicators. These primary indicators complement the common and applicable sustainable building indicators, ensuring a balanced approach that considers global contexts. DEMATEL analysis confirmed the validity and interconnection of these indicators, emphasizing the critical role of vernacular principles in achieving true sustainability.

The COVID-19 pandemic has negatively impacted the industrial, financial, and social aspects of our daily life due to the implementation of lockdown to protect against the spread of the virus. In addition, the lockdown deduced by COVID-19 has promising positive impacts on air quality and environmental pollution. This study aims to monitor the effects of lockdown on environmental degradation during the pandemic in Kabul city, the capital of Afghanistan, using geospatial data and a statistical model of the Analytical Hierarchy Process (AHP). To achieve the purpose of the study, the most essential influencing factors on air quality were generated from different sources using Google Earth Engine (GEE) and GIS environment; Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index NDMI) were calculated using Sentinel-2MSI, Carbon Monoxide (CO) was obtained from Sentinel-5P TROPOMI, and land surface temperature was retrieved from MODIS data. The generated thematic layers (before COVID-19, and during a lockdown of COVID-19) were weighted and rated using the AHP analysis. The weighted layers were spatially overlayed to obtain the final output. Consequently, the environmental quality degradation maps before and during COVID-19 were generated to assess the differences over the 22 districts of Kabul city. The findings of the study show that Kabul city is covered by the very low, low, moderate, high, and very high degradation of the environment by 3.17%, 5.33%, 20.54%, 26.63%, 44.32% before COVID-19 in 201,9 respectively, while the percentages are changed to 4.37%, 8.99%. 16.55%, 37.47%, and 32.62% during the lockdown caused by COVID-19 in 2020. The changes in the percentage of environmental degradation in Kabul city particularly in high and very high zones confirm the positive impact of the lockdown of COVID-19.

Natural disasters are increasing in frequency and intensity, causing escalating humanitarian crises and complex housing challenges globally. Traditional post-disaster housing solutions often fall short, being slow, costly, and ill-adapted to specific community needs. This study addresses these limitations by proposing an innovative, technology-driven model for post-disaster housing that integrates parametric design with 3D printing. The objective is to develop a flexible and adaptable system capable of providing both immediate temporary shelter and evolving permanent housing solutions. In this study, the methodology of the proposed model for post-disaster housing solutions is structured around three main phases: the development of the theoretical framework, the parametric design process, and the implementation phase. In the first phase, a comprehensive literature review and conceptual analyses were conducted to examine the concept of disaster, post-disaster housing approaches, and advanced technologies, thereby establishing the conceptual foundation of the model. In the second phase, parametric modeling was carried out for a modular system using algorithmic design tools such as Grasshopper; the model’s applicability across various scales and its flexibility were analyzed. In the final phase, material selection and digital prototyping of the gridal system were undertaken using 3D printing technology to evaluate the model’s feasibility for rapid on-site production, assembly, and disassembly. The model prioritizes user participation, modularity, and configurability to ensure rapid response and socio-cultural sensitivity. Findings indicate that this integrated approach offers substantial benefits, including accelerated construction, reduced labor and material waste, enhanced design flexibility, and the use of local, sustainable materials. This research highlights the transformative potential of advanced manufacturing in providing resilient, user-centered, and environmentally sustainable post-disaster housing, advocating for governmental financial support to overcome adoption barriers and foster broader implementation.