Abstract: The construction industry faces significant challenges in resource allocation, workforce management and project scheduling, leading to cost overruns and delays. Traditional Building Information Modeling (BIM) approaches lack intelligent decision-making capabilities for dynamic resource optimization. This research presents a novel framework integrating machine learning (ML) and deep learning (DL) techniques with BIM for intelligent resource and workforce management in construction projects. The proposed system utilizes Support Vector Machines (SVM), Random Forests and Convolutional Neural Networks (CNN) to predict resource requirements, optimize workforce allocation and automate construction scheduling. The framework processes historical construction data through a multi-layered architecture that combines BIM model data with real-time project parameters. Experimental validation using the PSPLIB dataset and real-world construction projects demonstrates significant improvements in resource utilization efficiency (25% improvement), schedule accuracy (18% reduction in delays) and cost optimization (15% reduction in project costs) compared to traditional methods. The system achieved 89% accuracy in predicting resource requirements and 92% precision in workforce allocation decisions. Deep learning models showed superior performance in clash detection and conflict resolution, achieving 95% accuracy in identifying potential construction conflicts. The integration of predictive analytics with BIM data enables proactive decision-making, reducing manual intervention by 40% and improving overall project delivery timelines. This research contributes to the advancement of intelligent construction management systems and provides a foundation for future development of autonomous project management platforms.
Keywords: Building Information Modeling, Machine Learning, Deep Learning, Resource Optimization, Workforce Management, Construction Scheduling, Predictive Analytics, Intelligent Construction
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DOI:
10.17148/IJARCCE.2025.14594
