From Ambiguity to Execution: An Agentic Neuro-Symbolic Framework for Transforming Building Regulations into Deterministic Constraints

Abstract

The transition toward fully autonomous Digital Building Permitting (DBP) requires Automated Compliance Checking (ACC) systems to verify Building Information Models (BIM) against natural-language regulations. While Large Language Models (LLMs) offer strong semantic comprehension, integrating them into ACC introduces “Spatial Hallucinations” and “Serialization Bottlenecks” when processing high-dimensional BIM graphs. This research proposes an Agentic Neuro-Symbolic Framework that decouples semantic interpretation from geometric verification. Instead of relying on generative text for spatial reasoning, an Agentic LLM acts as a dynamic logic synthesizer orchestrating a deterministic geometry kernel (IfcOpenShell). The artifact was evaluated against the Australian National Construction Code (NCC 2022) across three stratified tiers: Semantic-Geometric Alignment, Multi-Parametric Dependencies, and Relational Topology Reasoning. Results demonstrate the framework autonomously resolves ontological ambiguity and synthesizes execution logic dynamically. By implementing connectivity graph traversal, the system isolates structural sub-graphs, reducing computational complexity from O(N) to O(K) and bypassing context-window limits. Offloading calculations to a deterministic environment achieves a highly deterministic accuracy rate for spatial queries, yielding immutable BIM Collaboration Format (.bcfzip) audit trails. Ultimately, this research establishes a scalable foundation for autonomous compliance, demonstrating that AI reliability in engineering significantly improves when probabilistic models orchestrate deterministic tools rather than predicting physical realities.