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Author:

Benwei Hou (Benwei Hou.) | Qianyi Xu (Qianyi Xu.) | Zilan Zhong (Zilan Zhong.) | Junyan Han (Junyan Han.) | Huiquan Miao (Huiquan Miao.) | Xiuli Du (Xiuli Du.)

Abstract:

The reliability assessment of pipeline networks under nature disasters helps to improve the disaster prevention capability and overall resilience of pipeline networks. For the earthquake hazard, the seismic damage of pipelines in the network is usually correlated due to the spatial correlation of critical parameters, including characteristics of pipeline, soil properties and ground motions. This paper proposes a framework to evaluate the seismic reliability of pipeline network under spatially correlated parameters based on the quasi-Monte Carlo (QMC) simulation. The influence of spatial correlated random variables on the seismic reliability of pipeline network is investigated with explicit consideration of the correlated random variables including the peak ground velocity, the wall thickness and the yield stress of pipe segment. The framework has been implemented in seismic reliability evaluation of two pipeline networks. The result shows that the QMC method has better simulation accuracy than the standard MC method under same sampling numbers. The spatial correlations have different influences on the seismic connectivity reliability of pipeline networks with different topological redundancy and parallelism of connective paths from sources to user nodes. For the pipeline network with greater redundancy and parallelism, the model without considering the spatial correlations overestimates the network connectivity reliability.

Keyword:

Nataf transformation pipeline networks quasi-Monte Carlo simulation Seismic reliability spatial correlation Sobol’s sequence

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Source :

Structure and Infrastructure Engineering

ISSN: 1573-2479

Year: 2024

Issue: 4

Volume: 20

Page: 498-513

3 . 7 0 0

JCR@2022

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count: -1

Chinese Cited Count:

30 Days PV: 4

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