Architecture Engineers Award

In response to accelerating global challenges such as resource depletion, climate risk, and urban health inequality, architectural design is undergoing a fundamental shift from experience-based approaches to intelligence-driven practices. Artificial Intelligence for Sustainable Architectural Design (AI4SAD) has emerged as a critical catalyst in this transformation. This study provides the first comprehensive mapping of AI4SAD research, examining how AI is being applied across architectural design stages, sustainability objectives, and algorithmic domains. Scope and Methodology of the Systematic Review The research is grounded in a systematic review of 408 scholarly studies, offering a robust spatiotemporal overview of global AI4SAD development. By analyzing patterns across regions, time periods, design phases, and sustainability targets, the study establishes a structured evidence base that reveals dominant research trajectories as well as underexplored areas within AI-driven sustai...

The construction sector continues to struggle with long-standing challenges related to low productivity, limited innovation, and fragmented organizational structures. While Artificial Intelligence (AI) presents significant opportunities for transformation under the paradigm of Smart Buildings and Construction 4.0 (SBC4.0), its implementation at the organizational level remains insufficiently understood. This study addresses this gap by examining how construction firms adopt AI and the organizational forces shaping these decisions. Theoretical Foundations for AI Implementation The research develops an integrated theoretical framework drawing on institutional theory, the resource-based view, and dynamic capabilities. Institutional theory explains how external pressures influence organizational behavior, while the resource-based view and dynamic capabilities highlight the internal assets and adaptive capacities required for AI adoption. Together, these perspectives provide a comprehens...

With the rapid advancement of digitalization and artificial intelligence , architectural design faces a fundamental limitation: the dominance of geometric representations with insufficient semantic depth. This imbalance restricts the interpretability, reasoning capacity, and transferability of AI-driven design systems across the building lifecycle. Addressing this challenge, the present study explores how architectural geometry models can be semantically enhanced to support AI-based modeling and reasoning, laying the foundation for more intelligent, explainable, and integrated design processes. Limit thinking in Geometry-Centric Architectural Models The study critically examines the limitations of conventional geometric representations across the design–performance–construction chain. Through bibliometric analysis and an extensive literature review, it reveals how geometry-only models fail to capture intent, performance logic, construction rules, and associative knowledge. These shor...

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