Multi‐scale seismic reliability assessment of networks by centrality‐based selective recursive decomposition algorithm. (7th March 2021)

• Record Type: Journal Article
• Authors: Lee, Dongkyu; Song, Junho
• Is Part Of: Earthquake engineering and structural dynamics. Volume 50:Number 8(2021)
• Page Start: 2174
• Page End: 2194
• Publication Date: 2021-03-07
• Subjects: betweenness centrality -- clustering -- modularity -- multi‐scale approach -- network reliability -- non‐simulation‐based approach -- recursive decomposition algorithm -- seismic reliability; Structural dynamics -- Periodicals; Earthquake engineering -- Periodicals; 624.1762
• DOI: 10.1002/eqe.3447
• Languages: English
• ISSNs: 0098-8847

Abstract

Abstract: As lifeline networks such as transportation or electricity networks in modern societies are intricately interlocked, a small number of components damaged by natural or man‐made disasters can have a great impact on network performance. For this reason, it is essential to assure the capability of rapid assessment of network reliability to make prompt follow‐up measures. Despite the rapid development of various algorithms and computing power, the capability is still limited due to computational cost for analyzing the connectivity of a single origin and destination (O/D) node pair in large‐scale networks. Therefore, this paper introduces a new algorithm utilizing network centrality, termed "centrality‐based selective recursive decomposition algorithm" (CS‐RDA). By preferentially decomposing the node which is most likely to belong to the min‐cut identified based on the betweenness centrality, the convergence of the bounds on the O/D connectivity can be expedited significantly. This paper also introduces a new multi‐scale analysis approach termed "edge‐betweenness algorithm." The algorithm groups components such that its modularity is maximized, by sequentially removing edges that have the highest level of betweenness centrality. As a result, the reliability of large‐scale networks can be accurately evaluated in a short time owing to the reduced complexity of the simplified network. The proposed methods are successfully demonstrated by a hypothetical network example, the highway bridge networks in San Jose and San Diego in California, USA.