Optimizing resource efficiency and innovation in construction with BIM integration: A hybrid ANN-SEM approach

This study investigates the influence of Building Information Modelling (BIM) on optimizing resource efficiency and driving innovation within construction activities, focusing on a case study of Kuwait’s construction industry. The research adopts a unified analytical framework that combines Partial Least Squares–Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) approaches to capture both linear and nonlinear relationships among critical factors influencing BIM implementation. Data were collected from 151 construction professionals through standardized questionnaires and expert consultations, emphasizing five key dimensions: Design Optimization, Material Quantification and Tracking, Energy Analysis, Lifecycle Assessment, and Prefabrication. The PLS-SEM results reveal that all five constructs significantly influence BIM adoption, with Design Optimization and Lifecycle Assessment emerging as the strongest predictors. The subsequent ANN analysis validates these relationships and ranks the relative importance of the predictors, confirming the model’s robustness and predictive accuracy. The findings demonstrate that BIM adoption substantially enhances construction efficiency, sustainability, and innovation by improving resource utilization and minimizing waste. This Kuwait-based case study contributes to the growing body of knowledge by providing an empirically validated BIM Resource Efficiency Framework tailored to the regional context, offering practical insights for policymakers and industry stakeholders aiming to advance sustainable construction practices.