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Approximate Bayesian network formulation for the rapid loss assessment of real-world infrastructure systems

机译：近似贝叶斯网络公式，用于对实际基础架构系统进行快速损耗评估

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摘要

This paper proposes to learn an approximate Bayesian network (BN) model from Monte-Carlo simulations of an infrastructure system exposed to seismic hazard. Exploiting preliminary physical simulations has the twofold benefit of building a drastically simplified BN and of predicting complex system performance metrics. While the approximate BN cannot yield exact probabilities for predictive analyses, its use in backward analyses based on evidenced variables yields promising results as a decision support tool for post-earthquake rapid response. Only a reduced set of infrastructure components, whose importance is ranked through a random forest algorithm, is selected to predict the performance of the system. Further, owing to the higher importance of evidenced nodes, the ranking method is enhanced with a recursive evidence-driven BN-building algorithm, which iteratively inserts evidenced components into the subset identified by the random forest algorithm. This approach is applied to a French road network, where only 5 to 10 components out of 58 are kept to estimate the distribution of system performance metrics that are based on traffic flow. Sensitivity studies on the number of selected components, the number of off-line simulation runs and the discretization of variables reveal that the reduced BN applied to this specific example generates trustworthy estimates.
机译：本文建议从暴露于地震危险的基础设施系统的蒙特卡洛模拟中学习近似贝叶斯网络（BN）模型。利用初步的物理模拟具有建立大大简化的BN和预测复杂的系统性能指标的双重好处。虽然近似的BN不能产生用于预测分析的确切概率，但将其用于基于证据变量的后向分析中，可以产生有希望的结果，作为地震后快速响应的决策支持工具。仅选择一组减少的基础结构组件，这些组件的重要性通过随机森林算法进行排名，以预测系统的性能。此外，由于证据节点的重要性更高，因此通过递归证据驱动的BN构建算法增强了排序方法，该算法将证据成分迭代地插入到由随机森林算法标识的子集中。这种方法适用于法国的道路网络，在58个道路网络中，仅保留了5到10个组件来估计基于交通流量的系统性能指标的分布。对选定组件的数量，离线模拟运行的数量以及变量离散化的敏感性研究表明，应用于此特定示例的BN减少产生可信的估计。

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