Using Bayesian networks for the assessment of underwater scour for road and railway bridges
Using Bayesian networks for the assessment of underwater scour for road and railway bridges
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DOI：
10.1177/1475921720956579
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发表时间：
2018-05
期刊：
影响因子：
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通讯作者：
Andrea Maroni;E. Tubaldi;D. Val;H. McDonald;D. Zonta
中科院分区：
文献类型：
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作者：
Andrea Maroni;E. Tubaldi;D. Val;H. McDonald;D. Zonta
洪水引起的冲刷是世界范围内桥梁破坏的最常见的外部原因之一。在美国，冲刷是每年22座桥梁倒塌的原因，而在英国，在上个世纪，冲刷对138座桥梁的倒塌起着重要作用。冲刷评估目前是基于目视检查，这是耗时和昂贵的。如今，传感器和通信技术提供了真实的时间在关键桥梁位置的冲刷深度评估的可能性;然而，监测整个基础设施网络是不经济可行的。克服这种限制的一种方法是在关键桥梁位置安装冲刷监测系统，然后利用系统中存在的相关性将获得的信息扩展到其他资产。在这篇文章中，我们提出了一个冲刷危害模型的公路和铁路桥梁冲刷管理，利用信息从数量有限的冲刷监测系统，以实现更有限的估计冲刷风险的桥梁网络。贝叶斯网络被用来描述所涉及的随机变量之间的条件依赖关系，并更新冲刷深度分布的冲刷和河流流量特性的监测数据。本研究首次将贝氏网路应用于桥梁冲刷风险评估。将所提出的概率框架应用于由苏格兰几座公路桥组成的案例研究。这些桥梁跨越同一条河流，只有其中一座装有冲刷监测系统。它演示了如何贝叶斯网络的方法允许显着减少冲刷深度的不确定性在未监测的桥梁。
Flood-induced scour is among the most common external causes of bridge failures worldwide. In the United States, scour is the cause of 22 bridges fails every year, whereas in the UK, it contributed significantly to the 138 collapses of bridges in the last century. Scour assessments are currently based on visual inspections, which are time-consuming and expensive. Nowadays, sensor and communication technologies offer the possibility to assess in real time the scour depth at critical bridge locations; yet, monitoring an entire infrastructure network is not economically feasible. A way to overcome this limitation is to instal scour monitoring systems at critical bridge locations, and then extend the piece of information gained to the other assets exploiting the correlations present in the system. In this article, we propose a scour hazard model for road and railway bridge scour management that utilises information from a limited number of scour monitoring systems to achieve a more confined estimate of the scour risk for a bridge network. A Bayesian network is used to describe the conditional dependencies among the involved random variables and to update the scour depth distribution using data from monitoring of scour and river flow characteristics. This study constitutes the first application of Bayesian networks to bridge scour risk assessment. The proposed probabilistic framework is applied to a case study consisting of several road bridges in Scotland. The bridges cross the same river, and only one of them is instrumented with a scour monitoring system. It is demonstrated how the Bayesian network approach allows to significantly reduce the uncertainty in the scour depth at unmonitored bridges.