Simulation-Based Validation of an Integrated 4D/5D Digital-Twin Framework for Predictive Construction Control

TL;DR

This paper introduces a digital-twin framework combining BIM, AI, and probabilistic methods to improve construction project forecasting, control, and cost-schedule alignment, demonstrated through a nine-month case study.

Contribution

It presents an integrated 4D/5D digital-twin framework that combines BIM with AI and probabilistic techniques for enhanced construction project management.

Findings

43% reduction in labor estimating

6% reduction in overtime

30% project-buffer utilization

Abstract

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.