Objective: This paper seeks to provide an effective and automated method for the creation and updating of building information modeling compliance rules using the integration of human-in-the-loop collaboration with advanced natural language processing. Methods: We propose a hybrid approach that integrates BERT-based semantic extraction, CFG structural validation, and confidence-based expert review. Results: Tested on a real-world power infrastructure project, the framework was found to be 95.8% accurate in translation and 98.3% feasible in rule execution, outperforming benchmark automated approaches. The human effort was reduced by 90% (168 h vs. 1620 h), and processing of regulatory changes was sped up by 94%. Conclusion: Data analysis shows that collaborative intelligence is a significant factor in closing the semantic and pragmatic gap for regulatory compliance. Compared with fully automated “black box” approaches, this method supplies a tractable, manageable, and operationally valid solution, giving a competitive edge over existing digital construction methods.
Zhang et al. (Tue,) studied this question.