A hybrid reasoning framework based on construction safety requirements for responding to safety risks: incorporating natural language processing techniques

Purpose Effective response to safety risks is critical to reducing the incidence of safety accidents and is an important element of project management on construction sites. The objective of this study is to propose a hybrid reasoning framework for generating safety risk response strategies based on construction safety requirements (SRs) and incorporating specific measures for effective response to potential risks. Design/methodology/approach This study proposes a hybrid reasoning framework based on SRs for generating risk response strategies that align with safety risk characteristics. The framework integrates rule-based reasoning (RBR) and case-based reasoning (CBR) while leveraging knowledge transformation and the reuse of domain ontologies to enhance the effectiveness of safety risk responses. Additionally, natural language processing (NLP) techniques are employed to identify safety risk factors, which are then analyzed using the multidimensional attribute Apriori (MA-Apriori) algorithm to determine their association with SRs. Meanwhile, RBR is used to design three types of reasoning rules by combining the relationship between safety risks and different classes in the domain ontology, and then case similarity is measured by CBR to obtain the risk response strategies under the corresponding reasoning rules. Findings A total of 209 accident case files were used for the testing of safety risk responses, and 34 safety risk factors and corresponding reasoning rules were obtained from the source cases with high relevance, whereas the safety risk prevention measures and safety accident handling measures correspond to the reasoning rules generated in Protégé 5.5.0. Originality/value This study proposes a new paradigm to improve the performance of safety management by expanding the derived value of SRs to establish relationships with safety risks and to respond effectively to combined risk characteristics.