Simulation-Based Validation of an Integrated 4D/5D Digital-Twin Framework for Predictive Construction Control
This addresses cost and schedule control problems for the U.S. construction industry, representing an incremental improvement by integrating existing AI methods with probabilistic techniques.
The study tackled persistent cost and schedule deviations in construction by developing an integrated 4D/5D digital-twin framework, resulting in a 43% reduction in estimating labor, 6% reduction in overtime, and 30% project-buffer utilization while maintaining an on-time finish.
Persistent cost and schedule deviations remain a major challenge in the U.S. construction industry, revealing the limitations of deterministic CPM and static document-based estimating. This study presents an integrated 4D/5D digital-twin framework that couples Building Information Modeling (BIM) with natural-language processing (NLP)-based cost mapping, computer-vision (CV)-driven progress measurement, Bayesian probabilistic CPM updating, and deep-reinforcement-learning (DRL) resource-leveling. A nine-month case implementation on a Dallas-Fort Worth mid-rise project demonstrated measurable gains in accuracy and efficiency: 43% reduction in estimating labor, 6% reduction in overtime, and 30% project-buffer utilization, while maintaining an on-time finish at 128 days within P50-P80 confidence bounds. The digital-twin sandbox also enabled real-time "what-if" forecasting and traceable cost-schedule alignment through a 5D knowledge graph. Findings confirm that integrating AI-based analytics with probabilistic CPM and DRL enhances forecasting precision, transparency, and control resilience. The validated workflow establishes a practical pathway toward predictive, adaptive, and auditable construction management.