A New Risk Response Strategy Using Association Rule Mining and Building Information Modeling Capabilities

The dynamic and complex nature of the construction industry leads to increased project uncertainty, exposing construction projects to various risks and hazards. Poor risk management can hinder project objectives. Therefore, implementing effective risk management strategies can enhance project quality, safety, and ensure on-time, under-budget completion. This is achievable when the construction industry adopts cutting-edge methods and tools. Building information modeling (BIM) has been widely used to facilitate project risk management due to rapid technological advancements. Given the significance of risk management in construction projects, this study has proposed a novel BIM-based expert system for addressing project risk responses. Data were collected through a questionnaire, and hidden patterns were discovered using SPSS Modeler software (Clementine) through association rule mining. The Apriori algorithm extracted fifty-three top rules from the dataset based on rule evaluation indexes. Subsequently, an expert system was developed using the extracted rules to address project risks. Finally, the expert system was evaluated by five unbiased experts through a questionnaire. This study can serve as a foundation for addressing project risks using BIM and data mining. Subsequent research can apply this method to other construction projects and compare the results with the present study.