Automating Geometry-Intensive Compliance Checking in BIM: Graph-Based Semantic Reasoning Framework

arXiv cs.AI Papers

Summary

This paper introduces SGR-BIM, a graph-driven semantic reasoning framework that dynamically aligns regulatory intent with BIM geometry to automate geometry-intensive compliance checks, achieving 84.3% accuracy on fire safety code queries.

arXiv:2606.12065v1 Announce Type: new 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.
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# Automating Geometry-Intensive Compliance Checking in BIM: Graph-Based Semantic Reasoning Framework
Source: [https://arxiv.org/abs/2606.12065](https://arxiv.org/abs/2606.12065)
[View PDF](https://arxiv.org/pdf/2606.12065)

> 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\.

## Submission history

From: Zixuan Xiao \[[view email](https://arxiv.org/show-email/f75e9e73/2606.12065)\] **\[v1\]**Wed, 10 Jun 2026 13:31:43 UTC \(8,162 KB\)

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