Architecture Engineers Award

  Early-stage architectural design is highly dependent on precedent cases and accumulated domain knowledge, which help designers explore concepts, establish design logic, and reduce uncertainty. However, existing digital assistance tools struggle to effectively support this phase due to the dominance of visual information and the linguistic diversity found in architectural descriptions. This study addresses these challenges by proposing a retrieval-augmented generation (RAG) framework specifically tailored to architectural design contexts. Challenges in Precedent-Based Architectural Assistance Architectural precedents are complex, multimodal, and context-sensitive, combining drawings, images, diagrams, and textual narratives. Traditional retrieval systems often fail to capture the underlying design logic or to align visual data with semantic descriptions. These limitations reduce retrieval accuracy and restrict the usefulness of precedent recommendations during conceptual desig...

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...