🤖 AI Summary
This study addresses the challenge of automating geometric compliance checking in Building Information Models (BIM), where existing methods struggle with multi-hop reasoning and cross-component spatial dependencies due to a semantic gap. To overcome this, the authors propose SGR-BIM—the first graph-driven semantic reasoning system that dynamically constructs a cross-modal knowledge graph integrating user intent, regulatory semantics, and BIM geometry. This approach enables interpretable, hard-coding-free compliance reasoning, transcending the limitations of static rule templates and supporting flexible, transparent geometric validation. Evaluated on 679 expert-verified fire safety regulation queries, SGR-BIM achieves an accuracy of 84.3%, outperforming an enhanced single-agent baseline by 8.6%.
📝 Abstract
Automating compliance check for geometry-intensive regulations remains a significant technical bottleneck in Building Information Modeling (BIM), primarily due to the semantic disparity between high-level regulatory logic and structured IFC data. Existing methods, often reliant on static rule templates, struggle to traverse multi-hop reasoning chains or resolve latent spatial dependencies across multiple building entities. To address these challenges, a Spatial-Geometric Reasoning System for Building Information Modeling (SGR-BIM) is proposed as an integrative graph-driven reasoning framework. SGR-BIM dynamically constructs a cross-modal knowledge graph that aligns user intent, regulatory semantics, and BIM geometry, enabling interpretable reasoning without rigid hard-coding. Validated on 679 expert-verified queries from fire safety codes, the framework achieves 84.3% accuracy, representing an 8.6% improvement over enhanced-tool single-agent baselines. This research provides a graph-based semantic reasoning paradigm, enhancing the transparency and flexibility of automated geometric compliance check workflows in the Architecture, Engineering, and Construction (AEC) industry.