Probabilistic modelling of tunnel excavation processes using a Bayesian network

拟议的研究是Hatch为优化和完成16号高速公路的建造而做出的更大努力的一部分 为力拓阿尔坎铝冶炼厂供电的水电设施的备用隧道长达1公里 公元前基蒂马特。这项研究将解决如何使用历史设计、地质制图和挖掘 从50年代S和90年代S现场修建的隧道记录确定最佳当前隧道开挖 技术，并预测隧道掘进速度和成本。这项研究将帮助Hatch提供 向力拓加拿大铝业提出关于适当的隧道施工方法和成本的风险知情建议， 以及确定可能出现挖掘困难的区域。拟议的研究将使用贝叶斯 网络框架，其中影响隧道开挖的参数可以进行评估并用于制定更多 隧道施工过程中准确的进度和成本预测。贝叶斯网络模型将 开发将结合地质预测模型(以预测隧道前方的地质条件 Face)和建筑策略决策模型，将风险降至最低。贝叶斯网络有能力 将专家知识与观察数据相结合，表达变量之间的依赖关系，并具有学习能力 并完善因果关系。这项研究应该为规划者和工程师提供一个强大的工具 用于系统地评估和缓解与隧道建设相关的固有风险。

The proposed research is a part of a larger effort by Hatch to optimize and complete the construction of a 16 km long backup tunnel for the hydroelectric facility providing power to Rio Tinto Alcan's aluminum smelter at Kitimat BC. The research will address how to use historical designs, geological mapping, and excavation records from tunnels constructed at the site in the 1950's and the 1990's to determine the best current tunnelling technologies and to predict the tunnelling advance rates and costs. This research will help Hatch provide risk-informed recommendations to Rio Tinto Alcan concerning appropriate tunnelling methods and costs, as well as identifying areas where excavation difficulties could occur. The proposed research will use a Bayesian Network framework where parameters that affect tunnel excavation can be evaluated and used to make more accurate schedule and cost predictions during the tunnelling project. The Bayesian Network model that will be developed will combine a geological prediction model (to predict geological conditions ahead of the tunnel face) with a construction strategy decision model to minimize risk. A Bayesian Network has the ability to combine expert knowledge with observed data, express dependencies among variables, and the ability to learn and refine causal relationships. This research should provide a powerful tool that planners and engineers can use to systematically assess and mitigate the inherent risks associated with tunnel construction.