NLP-based approach for automated safety requirements information retrieval from project documents

Abstract

Numerous project documents generated during the construction phase pertain to the actual safety requirements (SRs) specified by project managers (PMs), and these concealed requirements can aid in making safety decisions if accurately identified and utilized. However, PMs’ requirements are frequently recorded informally in project documents, necessitating extensive manual analysis to guide safety management practices. To address this limitation, this study developed a natural language processing (NLP)-based framework of requirement retrieval and document association (RRDA) to mine requirements and retrieve requirement-related documents. In particular, requirement-document (RD) association rules are designed to retrieve the requirement-related documents. The results demonstrate that our framework can retrieve the PMs’ requirements with a maximum ontology relevance of 91.37% and emotional preference of requirement with a maximum semantic tendency intensity of 0.91. The presented algorithm shows satisfactory performance in the number of iterations and threshold, and there are outstanding advantages in model training comparison. Additionally, there is a significant degree of matching between the retrieved documents and the requirements, which has significant managerial implications for requirement-oriented construction safety management.