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Structure and Infrastructure Engineering
Maintenance, Management, Life-Cycle Design and Performance
Volume 20, 2024 - Issue 4
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Articles

Seismic reliability evaluation of spatially correlated pipeline networks by
quasi-Monte Carlo simulation

ORCID Icon, ORCID Icon, , ORCID Icon, &
Pages 498-513 | Received 26 Feb 2022, Accepted 01 Aug 2022, Published online: 15 Aug 2022
 

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.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research work was supported by the National Natural Science Foundation of China (NSFC) (Grant No. 51978023) and the Science and Technology Planning Project of Beijing Municipal Education Commission (Grant No. KM202010005031).

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