Copula-based multivariate renewal model for life-cycle analysis of civil infrastructure considering multiple dependent deterioration processes

Yaohan Li, You Dong and Hongyuan Guo

Reliability Engineering and System Safety, 2023, vol. 231, issue C

Abstract: Civil infrastructure is subjected to multiple deterioration processes (e.g., gradual deterioration and shock deterioration) caused by environmental exposure and extreme events during its lifetime. To maintain performance and functionality, maintenance actions should be performed and the life-cycle cost may be affected. There is a need to explore the effect of maintenance actions and various uncertainties on the life-cycle performance of the engineering systems. This study proposes a probabilistic life-cycle analysis framework for civil infrastructure based on performance indicators, e.g., reliability and maintenance cost. Stochastic uncertainties resulting from multiple dependent deterioration processes, system reliability, intervention actions, and maintenance cost are considered. In particular, the dependence between the maintenance interval and cost is highlighted. Previous studies generally assume they are independent. Such an assumption can be misleading and lead to inappropriate cost estimation. To address this concern, a copula-based multivariate renewal model is proposed to assess the life-cycle maintenance cost analytically and numerically. In addition to the expected cost, statistical moments (e.g., standard deviation, skewness, and kurtosis) are calculated to quantify uncertainties from higher-order moments. Two illustrative examples show that the dependence and uncertainties can have a large impact on the life-cycle cost, and decisions can be altered by considering statistical moments of the cost.

Keywords: Copula; Life-cycle cost; Higher-order moments; Stochastic deterioration; Structural reliability (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:231:y:2023:i:c:s095183202200607x

DOI: 10.1016/j.ress.2022.108992

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