Purpose This study aims to comprehensively assess the transferability of optimized values of independent design variables from an early-stage single-storey model to a detailed whole building and to identify the most influential design variables to reduce thermal discomfort, cooling energy and improve daylight for a residential building in the warm and humid region of Chennai. Design/methodology/approach This study consists of six phases. In the initial phase, the simulation results were validated using field measurements. Four design cases were developed for the early to detailed design stages, and multi-objective optimization (MOO) was performed for each design case. Multiple regression analysis was performed to identify the most significant (p < 0.05) independent design variables for the performance objectives, and the magnitude of their influence was measured using the standardized coefficient beta (ss). A deep learning sequential model was built for each design case, and the overall influence of the design variables was analyzed using permutation feature importance. Findings The results revealed that the cooling setpoint, energy efficiency of the air conditioner (ISEER) and window-to-wall ratios (N-S-E-W) exhibited greater transferability and variations in importance from the early to detailed design stages. Most of the independent design variables considered in this study showed both statistical significance and non-significance across the design cases. Research limitations/implications This study provides guidance for architects and engineers by emphasizing prioritizing the most influential independent design variables for achieving high-performance residential buildings. Originality/value This study combines statistical measures and permutation feature importance to highlight the sensitivity of design variables from the early to detailed design stages.
Purpose-Unregulated urban development, along with climate change, significantly alters ecological dynamics in cities. The purpose of this article is to address the systemic neglect of biodiversity and socio-spatial justice in Belgrade's urban development by proposing a novel model that redirects the course of adaptive reuse toward fostering socio-ecological resilience, in line with the UN sustainable development goals (SDGs). Design/methodology/approach-The research employs a research-through-design (RtD) methodology, positioning the architectural design process as the primary generator of knowledge and a tool for critical inquiry. Developed through a master design studio as a platform for action planning, the study treats the design of the Migratory Bird Station as a synthesizing mechanism that integrates ecological imperatives, industrial heritage and socio-spatial justice. It explores the potential for the adaptive reuse of Belgrade's obsolete Old Railway Bridge, specifically addressing the SDGs by creating a model that is socially just, ecologically sustainable and culturally enriching. Through iterative spatial exploration, the project develops a hybrid structure that promotes adaptive coexistence between people and nature, providing birds with safety in the urban environment and offering a viable socio-ecological alternative for the city. Findings-The proposed model demonstrates that research-based architectural design, founded on a place-and problem-based approach, represents a valuable alternative to conventional practices of built heritage conservation. The program actively promotes user participation through bird watching and ecological education. It is socially just in respect to user participation and inclusivity, ecologically sustainable as it provides birds habitat as a vital stop-over station, and culturally enriching, enhancing public awareness and responsibility toward non-human species. Originality/value-This project advances beyond standard ecological infrastructures by synthesizing RtD with animal-aided design in the context of industrial heritage. Unlike top-down eco-bridges, this model proposes a bottom-up approach that treats architectural design as an active research instrument for "wicked" urban problems. It positions neglected infrastructure as an active agent within a distributed ecological network. By reframing obsolete structures into socio-ecological habitats, the work offers a unique methodological bridge between multispecies urbanism and the spatial realities of post-industrial contexts like the Belgrade confluence.
Purpose-To develop and test a context-responsive green design framework through a systematic literature review and comparative multiple-case analysis of LEED-and BREEAM-certified buildings. Design/methodology/approach-A PRISMA-based systematic literature review identified 112 eligible studies from Scopus, Web of Science and ScienceDirect (2000-2025). Six eco-design domains were synthesized and operationalized through a matrix-based evaluation of The Edge (BREEAM Outstanding) and Bank of America Tower (LEED Platinum). Findings-Results reveal strong convergence in energy-related domains, while differences emerge in water management and indoor environmental quality. Sensitivity analysis confirmed the robustness of comparative rankings. Research limitations/implications-The study is limited to two internationally certified case studies and focuses on design-integrated ecological performance rather than broader socio-economic dimensions. Social implications-The framework provides architects, designers and policymakers with a transparent tool for benchmarking and improving sustainability integration in certified buildings. Originality/value-The study advances green building evaluation by integrating systematic evidence synthesis with matrix-based benchmarking and contributes a replicable framework for context-responsive green architectural assessment.
Purpose-This paper presents a design harmony evaluator tool designed to assist architects during early design stages in optimizing urban landscape aesthetics, which directly influence human psychology and behavior. By exploring the critical interplay between visual complexity and compositional rules, the research leverages deep learning (DL) to automate and classify building facade harmony based on established design laws. Design/methodology/approach-This paper presents a three-step approach to evaluating urban fa & ccedil;ade design harmony. First, images are annotated into harmonious and non-harmonious categories based on a geometric harmony scale. Second, visual complexity is quantified using Canny edge detection alongside Python-based fractal dimension analysis to investigate the correlation between complexity metrics and geometric harmony. Finally, pre-trained DL models are implemented in Python to automate fa & ccedil;ade harmony classification and assessment. Findings-DL architectures specifically InceptionResNetV2, InceptionV3, DenseNet121 and ResNeXt50 successfully automate the classification of fa & ccedil;ade harmony, achieving an 80% precision rate. Rather than replacing human intuition, this automated evaluator serves as a predictive design-support tool. Integrated into the early design stages, it allows architects to iteratively test facade design harmony. Research limitations/implications-The facade harmony evaluator could be used by all architects. However, researchers are encouraged to explore the development of similar tools based on the use of artificial intelligence (AI) to evaluate specific architectural styles or elements. Originality/value-This paper fulfills an identified need to study how AI and DL can help to create more harmonious facades and urban landscape. Unlike traditional workflows that evaluate visual complexity only after a facade layout is completed, this study inverts the design sequence by targeting core geometric composition at the earliest sketch stage. To achieve this, a dedicated geometric harmony scale was established to define the rules of compositional order. Finally, AI and DL architectures were leveraged to successfully translate this theoretical scale into a fully automated computational tool.
Purpose This study investigates whether housing plans generated by generative artificial intelligence (AI) reproduce the spatial logics embedded in established residential typologies and examines the implications of AI-driven housing design for spatial diversity and, more broadly, for discussions of cultural sustainability within the built environment (SDG 11). While prior research has focused largely on visual realism and functional plausibility, the configurational integrity of AI-generated housing remains underexplored. The study evaluates typological sensitivity and proposes a quantitative framework for assessing potential configurational compression in spatial organisation. Design/methodology/approach Residential plans representing three typologies – courtyard, row house and apartment – were generated using DALL·E 3 under controlled conditions and analysed using space syntax metrics (VGA, Integration, Choice and Connectivity). Principal component analysis (PCA) projected the plans into a configurational morphospace. Centroid separation, silhouette analysis and an exploratory configurational compression index (CCI) were employed to evaluate positional differentiation and cluster cohesion. Findings Configurational variation was largely organised along a single centralisation gradient (PC1 = 89.22%). Although centroid distances indicated measurable positional differentiation, the silhouette coefficient (0.272) and the exploratory CCI (0.335) suggested limited cluster cohesion and partial overlap among the analysed typologies. The findings suggest that AI-generated housing designs preserve recognisable visual typological characteristics while tending towards simplified relational spatial organisation within the analysed dataset. Originality/value The study introduces the concept of typological configurational compression and proposes an exploratory analytical framework integrating morphospace projection, cluster validation and compression indexing to evaluate configurational convergence in AI-assisted housing design.
