Intelligent Identification and Repair of Design Defects in BIM via Domain-Specific Large Language Models
A domain-specific LLM framework is proposed for end-to-end identification and repair of design defects in Building Information Modeling (BIM) A novel BIM-to-Text conversion method with component-balanced chunking bridges structured BIM data with LLMs Prompt learning combining rule injection, few-shot prompting, and RAG achieves 85% defect identification accuracy versus 70% for traditional rule checking A hallucination control strategy using key identifier validation and token-length thresholds e
Analysis
TL;DR
- A domain-specific LLM framework is proposed for end-to-end identification and repair of design defects in Building Information Modeling (BIM)
- A novel BIM-to-Text conversion method with component-balanced chunking bridges structured BIM data with LLMs
- Prompt learning combining rule injection, few-shot prompting, and RAG achieves 85% defect identification accuracy versus 70% for traditional rule checking
- A hallucination control strategy using key identifier validation and token-length thresholds eliminated 92.5% of hallucinations in a single round, raising accuracy from 64% to 85%
- The system generates repair suggestions at a 94% reasonable rate, establishing a complete pipeline from raw BIM input to actionable defect resolution
Why It Matters
This work addresses a critical gap in AEC (Architecture, Engineering, and Construction) workflows where manual or rule-based defect checking is labor-intensive and limited in scope. By demonstrating that domain-specific LLMs can outperform traditional rule-checking methods, it opens the door for intelligent automation in construction quality assurance—a $10 trillion global industry ripe for AI-driven transformation.
Technical Details
- BIM-to-Text with Component-Balanced Chunking: A novel data conversion pipeline that transforms structured BIM models into LLM-readable text while preserving component-level granularity, enabling LLMs to reason over spatial and semantic relationships in building designs.
- Multi-Strategy Prompt Learning: Combines rule injection (embedding domain-specific building codes and standards directly into prompts), few-shot prompting (providing curated defect examples), and Retrieval-Augmented Generation (RAG) to ground LLM outputs in verified knowledge bases.
- Hallucination Control Mechanism: A two-pronged validation strategy—key identifier validation checks that critical defect attributes match expected patterns, while token-length thresholds filter out overly verbose or speculative outputs—reducing hallucinations by 92.5%.
- End-to-End Prototype Architecture: The system processes raw BIM data through defect identification and repair suggestion generation in a single pipeline, with experimental results showing 85% identification accuracy and 94% reasonable repair suggestion rate.
Industry Insight
- Vertical AI in Construction: This demonstrates the viability of domain-specific LLMs in traditional industries, suggesting that AEC firms should invest in custom LLM pipelines rather than relying on generic models or legacy rule-based tools.
- Hallucination Control as a Deployment Prerequisite: The 92.5% hallucination reduction highlights that reliability mechanisms must be baked into production LLM systems for high-stakes domains—regulators and practitioners will demand this before adoption.
- Rule + LLM Hybrid as a Transitional Strategy: The combination of explicit rule injection with LLM reasoning offers a practical blueprint for organizations with established codebases, allowing incremental AI integration without discarding existing domain knowledge.
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