Generating Site-Responsive Architectural Forms Using Artificial Intelligence and Geotechnical Data

This study explores an integrated framework for generating site responsive architectural forms through the convergence of artificial intelligence techniques and high resolution geotechnical data. The proposed approach leverages machine learning models, including generative adversarial networks and transformer based spatial predictors, to translate subsurface conditions into morphologically adaptive design solutions. By embedding soil composition, stratigraphic variability, groundwater behavior, and load bearing capacity into a multidimensional dataset, the system enables the production of architectural geometries that respond dynamically to local geotechnical constraints. The research evaluates the performance of the AI driven design engine using comparative simulations, structural stability metrics, and form to substrate compatibility indices. Results demonstrate that AI generated forms exhibit significantly enhanced alignment with site specific geotechnical profiles, reducing foundational risks and minimizing material redundancy. Furthermore, the findings highlight the potential of integrating geotechnical intelligence with generative design pipelines to support early stage decision making, improve predictive accuracy, and enable robust adaptation across diverse terrains. Overall, this study establishes a methodological basis for advancing performance oriented architectural design, emphasizing the critical intersection of computational intelligence, environmental responsiveness, and subsurface-informed morphogenesis.

کلیدواژه ها:

Site-Responsive Architecture ، Geotechnical Data Integration ، AI-Driven Generative Design ، Machine-Learning ، Structural Optimization ، Subsurface-Informed Morphogenesis