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Seismic reliability assessment of lifeline networks using clustering-based multi-scale approach

机译:基于聚类的多尺度方法的生命线网络抗震可靠性评估

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Seismic reliability assessment of lifeline networks gives rise to various technical challenges, which are mostly caused by a large number of network components, complex network topology, and statistical dependence between component failures. For effective risk assessment and probabilistic inference based on post-hazard observations, various non-simulation-based algorithms have been developed, including the selective recursive decomposition algorithm (S-RDA). To facilitate the application of such an algorithm to large networks, a new multi-scale approach is developed in this paper. Using spectral clustering algorithms, a network is first divided into an adequate number of clusters such that the number of inter-cluster links is minimized while the number of the nodes in each cluster remains reasonably large. The connectivity around the identified clusters is represented by super-links. The reduced size of the simplified network enables the S-RDA algorithm to perform the network risk assessment efficiently. When the simplified network is still large even after a clustering, additional levels of clustering can be introduced to have a hierarchical modeling structure. The efficiency and effectiveness of the proposed multi-scale approach are demonstrated successfully by numerical examples of a hypothetical network, a gas transmission pipeline network, and a water transmission network.
机译:生命线网络的抗震可靠性评估带来了各种技术挑战,这些挑战主要是由大量的网络组件,复杂的网络拓扑以及组件故障之间的统计依赖性引起的。为了基于危险后的观察结果进行有效的风险评估和概率推断,已开发了各种基于非模拟的算法,包括选择性递归分解算法(S-RDA)。为了促进这种算法在大型网络中的应用,本文提出了一种新的多尺度方法。使用频谱聚类算法,首先将网络划分为足够数量的集群,以使集群间链接的数量最小化,同时每个集群中的节点数量保持相当大的水平。所标识的集群周围的连通性由超级链接表示。简化网络的减小的尺寸使S-RDA算法能够有效地执行网络风险评估。当即使经过聚类后简化网络仍然很大时,可以引入附加级别的聚类以具有分层的建模结构。通过假设网络,输气管道网络和输水网络的数值示例成功地证明了所提出的多尺度方法的效率和有效性。

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