Intelligent Identification and Correction of Property-related Design Defects in BIM via Large Language Models
Automation in Construction, 2026
Recommended citation: Lin, J.R., Cai, Y., Ni, X.R., Pan, P.* (2026). Intelligent Identification and Correction of Property-related Design Defects in BIM via Large Language Models. Automation in Construction, 192, 107218. doi: 10.1016/j.autcon.2026.107218 http://doi.org/10.1016/j.autcon.2026.107218
Abstract
Existing Building Information Modeling (BIM) checking methods focus on specific design defect types, while failing to identify and correct multi-type property-related BIM design defects in one loop. Therefore, this paper proposes an integrated framework to identify and correct multi-type property-related design defects in BIM via Large Language Models (LLMs). First, a BIM-to-Text method with component-balanced chunking is introduced to bridge BIM data and LLMs. Then, a prompt learning based method incorporating rule injection and few-shot prompting is proposed for defect identification, and a Retrieval Augmented Generation (RAG) method is established for correction suggestions. A hallucination control strategy combining key-identifier validation and token-length thresholds is developed to improve reliability. Experiments show that the proposed approach outperforms rule checking, and the fine-tuned LLM improves identification accuracy by 15%, with a 94% rationality rate for correction suggestions. Hallucination control increases accuracy from 64% to 85%, eliminating 92.5% of hallucinations in one round.
The authors are grateful for the financial support received from the National Key R&D Program of China (No. 2023YFC3804600) and the National Natural Science Foundation of China (No. 52378306).
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