For design for safety (DFS), automated compliance checking methods have received extensive attention. Although many research efforts have indicated the potential of BIM and ontology for automated compliance checking, an efficient methodology is still required for the interoperability and semantic representation of data from different sources. Therefore, a natural language processing (NLP)-based semantic framework is proposed in this paper, which implements rules-based automated compliance checking for building information modeling (BIM) at the design stage. Semantic-rich information can be extracted from safety regulations by NLP methods, which were analyzed to generate conceptual classes and individuals of ontology and provide a corpus basis for rule classification. The data on BIM was extracted from Revit to a spreadsheet using the Dynamo tool and then mapped to the ontology using the Cellfie tool. The interoperability of different source data was well improved through the isomorphism of information in the framework of semantic integration, causing data processed by the semantic web rule language to be transformed from safety regulations to achieve the purpose that automated compliance checking is implemented in the design documents. The practicability and scientific feasibility of the proposed framework was verified through a 95.21% recall and a 90.63% precision in compliance checking of a case study in China. Compared with traditional compliance checking methods, the proposed framework had high efficiency, response speed, data interoperability, and interaction.

Due to the complex and dynamic nature of construction environments, safety accidents occurring in these environments pose a grave threat to life and property. Therefore, it is essential for safety managers in construction, supervisory, and related units to adopt a rigorous and systematic methodology for assessing the risks associated with construction safety accidents. This will enable managers to comprehend the likelihood of accidents, subsequently enabling them to implement preemptive and control measures to reduce the probability of such incidents. Drawing on the accident causation theory, this study utilized web crawler technology to collect construction accident reports, subsequently employing text mining (TM) techniques to identify the accident causal factors specified in 166 accident reports. Subsequently, 33 key features were extracted from the accident causal factors, and correlation rule mining was used to analyze the correlations between the causal factors. Successively, a Bayesian network (BN)-based risk assessment model was constructed for construction safety accidents. Finally, through reverse reasoning, this study identified the probable paths of construction safety accidents and the sensitive factors that trigger such accidents. The results showed that management factors (MFs) are the primary drivers of accidents, highlighting the importance of focusing on preventive and control countermeasures for factors characterized with high severity and sensitivity.