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Bayesian-Based Hybrid Simulation Approach to Project Completion Forecasting for Underground Construction

机译：基于贝叶斯的混合仿真方法在地下工程完工预测中的应用

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摘要

Real-time simulation is powerful in forecasting the completion probability of long-term projects with repetitive tasks but fails to consider the time-varying uncertainty of inputs caused by construction process variabilities. In this paper, an improved method is introduced for predicting the time-varying probability of project completion of ongoing underground cavern group projects using Bayesian updating techniques. Within a tailor-made hierarchical simulation model, the Bayesian approach is adopted to constantly update duration distributions of unfinished project activities according to onsite data. The probability of project completion can therefore be increasingly refined during the process. The methodology is further explained in a case study where its feasibility and advantage over traditional approaches are verified. The success may also be replicated in addressing other similar time-varying uncertainty issues inherently present in almost all construction projects
机译：实时模拟在预测具有重复任务的长期项目的完成概率方面功能强大，但无法考虑由施工过程可变性引起的输入随时间变化的不确定性。本文介绍了一种改进的方法，利用贝叶斯更新技术预测正在进行的地下洞室群项目的项目完成时变概率。在定制的分层模拟模型中，采用贝叶斯方法根据现场数据不断更新未完成项目活动的持续时间分布。因此，可以在此过程中逐步完善项目完成的可能性。在案例研究中进一步解释了该方法，该方法验证了其比传统方法的可行性和优势。解决几乎所有建设项目中固有的其他类似时变不确定性问题也可以复制成功

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