用AI与数字孪生提升施工预测精度，实测节省43%人力估算误差。
Simulation-Based Validation of an Integrated 4D/5D Digital-Twin Framework for Predictive Construction Control
- 融合BIM与AI技术，实现成本、进度动态追踪与更新。
- 案例显示劳动估算误差降43%，加班减少6%，缓冲时间利用率提30%。
- 适合需要精准控制工期与预算的大型建筑项目团队。
持续的成本与工期偏差仍是美国建筑业的重大挑战，暴露出确定性CPM和静态文档估算的局限性。本研究提出一种集成4D/5D数字孪生框架，结合建筑信息模型（BIM）、基于自然语言处理（NLP）的成本映射、计算机视觉（CV）驱动的进度测量、贝叶斯概率化CPM更新以及深度强化学习（DRL）资源均衡算法。在达拉斯-沃思堡一座中高层建筑项目上实施九个月，实现显著改进：劳动力估算误差降低43%，加班减少6%，项目缓冲时间利用率达30%，且在128天内按计划完成，处于P50-P80置信区间。数字孪生沙盒支持实时‘假设分析’预测，并通过5D知识图谱实现可追溯的成本-进度对齐。结果表明，将AI分析与概率化CPM及DRL结合，可显著提升预测精度、透明度与控制韧性。该验证流程为实现预测性、自适应与可审计的施工管理提供了可行路径。
原文摘要 · Abstract (English)
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.
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