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

Researchers propose SGR-BIM, a graph-based AI framework that automates compliance checking for building regulations in BIM systems with 84.3% accuracy. The system bridges the gap between high-level regulatory logic and structured geometric data, addressing a major bottleneck in the Architecture, Engineering, and Construction industry.

The construction and engineering sectors face a persistent challenge in automating compliance verification for geometry-intensive building codes. Traditional rule-based systems rely on static templates that cannot effectively handle complex, multi-step reasoning across interconnected building components. SGR-BIM introduces a knowledge graph approach that dynamically maps regulatory semantics, user intent, and actual BIM geometry into a unified reasoning framework, moving beyond brittle hard-coded rules.

This advancement emerges from broader industry trends toward AI-driven automation in AEC workflows. The semantic gap between regulatory language and machine-readable data has long hindered productivity gains in compliance work, which typically involves expensive manual review by qualified professionals. Knowledge graph technologies, increasingly adopted across finance, healthcare, and manufacturing, now extend into construction domain intelligence.

The 8.6% performance improvement over baseline systems suggests meaningful practical value. Compliance checking represents significant labor cost and project timeline risk in construction—errors can trigger costly rework or regulatory penalties. Automating this process with interpretable AI reasoning could accelerate project delivery and reduce liability exposure for design and engineering firms.

Market adoption depends on integration with existing BIM platforms and vendor support. The framework's validation against 679 expert-verified fire safety code queries demonstrates domain-specific maturity, though scaling to other regulatory domains remains uncertain. AEC firms managing large portfolios or operating across multiple jurisdictions with varying codes stand to benefit most from such automation, potentially creating competitive advantages for early adopters.

• →SGR-BIM achieves 84.3% accuracy on fire safety compliance checks, outperforming single-agent baselines by 8.6%
• →Knowledge graphs enable the system to reason across multi-hop dependencies without rigid rule templates
• →The framework addresses a critical productivity bottleneck in AEC compliance workflows currently requiring expensive manual review
• →Interpretable reasoning improves transparency and reduces reliance on black-box AI systems in regulated construction environments
• →Broader adoption depends on integration with existing BIM platforms and extension to additional regulatory domains beyond fire codes