Construction projects frequently experience schedule delays and forecasting uncertainty due to variability in labor productivity, material availability, weather conditions, and project coordination. Conventional deterministic scheduling methods such as the Critical Path Method (CPM) assume fixed activity durations and therefore cannot adequately represent dynamic project uncertainty. This study presents a Bayesian-Monte Carlo probabilistic schedule updating framework for construction digital twin environments. The proposed methodology integrates stochastic activity-duration modeling, Bayesian recursive updating, Monte Carlo simulation, and uncertainty propagation within a unified computational framework for adaptive schedule forecasting. Activity durations are modeled using lognormal probability distributions and continuously updated through Bayesian inference as new project observations become available. Monte Carlo simulation is then used to propagate updated uncertainty throughout project networks and generate probabilistic completion-time forecasts, delay-risk estimates, and activity criticality measures. Simulation experiments using PSPLIB benchmark project networks demonstrate that the proposed framework improves forecasting accuracy and uncertainty representation compared with deterministic CPM and static probabilistic scheduling approaches. The framework further supports adaptive project forecasting through integration of BIM reports, drone observations, IoT telemetry, productivity logs, and site monitoring data.

翻译：施工项目因劳动力生产率、材料可用性、天气条件及项目协调等变量因素，常面临进度延误与预测不确定性。传统确定性调度方法（如关键路径法，CPM）假设活动工期固定，无法充分反映动态项目不确定性。本研究提出一种面向施工数字孪生环境的贝叶斯-蒙特卡洛概率调度更新框架。该方法将随机活动工期建模、贝叶斯递归更新、蒙特卡洛模拟与不确定性传播整合至统一计算框架内，实现自适应进度预测。活动工期采用对数正态概率分布建模，并通过贝叶斯推断随新项目观测数据持续更新。随后，蒙特卡洛模拟用于将更新后的不确定性传播至整个项目网络，生成概率性完工时间预测、延误风险估计及活动关键性指标。基于PSPLIB基准项目网络的仿真实验表明，与确定性CPM及静态概率调度方法相比，该框架提升了预测精度与不确定性表征能力。此外，该框架通过集成BIM报告、无人机观测、物联网遥测、生产率日志及现场监测数据，进一步支持自适应项目预测。