Measuring building information modeling user satisfaction by using active interpretable machine learning

Abstract

Accurately predicting building information modeling (BIM) user satisfaction (US) is essential for proactively addressing implementation challenges, ensuring effective adoption, and maximizing return on investment in BIM technologies in construction projects. Accordingly, this study developed advanced, interpretable boosting ensemble models to predict BIM US by integrating the forensic-based investigation (FBI) algorithm with gradient boosting machine, light gradient boosting machine, adaptive boosting (AdaBoost), extreme gradient boosting, and random forest algorithms. To validate the proposed models and establish a dataset, a comprehensive survey was conducted on 70 construction projects in Taiwan that used BIM technologies to support design work. Subsequently, the synthetic minority oversampling technique (SMOTE) was integrated into the proposed models to address the data imbalance problem. The results indicated that among all models, the FBI-AdaBoost-SMOTE model exhibited the highest performance, achieving accuracy, precision, recall, and F1 scores of 88.6 %, 90.6 %, 88.6 %, and 87.8 %, respectively. The FBI-AdaBoost model based on Shapley additive explanations identified contextual analysis and visualization, project scale, and cost estimates as key determinants of BIM US. Overall, this study presents an advanced machine learning framework for predicting BIM US and identifying key influencing factors for BIM US. It also provides actionable insights for stakeholders to enhance BIM implementation and user experience. In addition, this study highlights the potential of predictive modeling for optimizing the adoption of BIM in the architecture, engineering, and construction industry.