Simulation-Based Validation of an Integrated 4D/5D Digital-Twin Framework for Predictive Construction Control
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911251561447424 |
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| author | Khoshkonesh, Atena Mohammadagha, Mohsen Ebrahimi, Navid |
| author_facet | Khoshkonesh, Atena Mohammadagha, Mohsen Ebrahimi, Navid |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_03684 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Simulation-Based Validation of an Integrated 4D/5D Digital-Twin Framework for Predictive Construction Control Khoshkonesh, Atena Mohammadagha, Mohsen Ebrahimi, Navid Computational Engineering, Finance, and Science Artificial Intelligence Machine Learning Systems and Control 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. |
| title | Simulation-Based Validation of an Integrated 4D/5D Digital-Twin Framework for Predictive Construction Control |
| topic | Computational Engineering, Finance, and Science Artificial Intelligence Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2511.03684 |