Journal article
Reliability analysis of corrosion affected underground steel pipes considering multiple failure modes and their stochastic correlations
Tunnelling and underground space technology, Vol.87, pp.56-63
01/05/2019
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Source: InCites
Abstract
This paper presents a methodology for determining the probability of system failure of corrosion affected steel pipelines with multiple failure modes. The failure modes include fracture, strength failure, deflection and buckling and they are all modelled as stochastic processes with correlations among them. The first passage probability theory is employed to quantify the probability of pipe failure. A case study is presented to illustrate the proposed methodology; followed by a sensitivity analysis to investigate the effects of key random variables on the probability of pipe failure. It is found that correlation of the load effect process at different points in time and the correlation among different failure modes have a considerable effect on the probability of failure for each failure mode and probability of system failure respectively. It is also found that the corrosion model and pipe thickness are most influential with positive and negative indices respectively on the probability of pipe failure for all failure modes. The methodology presented in this paper can help pipe engineers and asset managers develop a risk-informed maintenance strategy for corrosion-affected steel pipelines.
Details
- Title
- Reliability analysis of corrosion affected underground steel pipes considering multiple failure modes and their stochastic correlations
- Creators
- Guoyang Fu - RMIT UniversityWei Yang - Victoria UniversityChun-Qing Li - RMIT UniversityWenhai Shi - RMIT University
- Publication Details
- Tunnelling and underground space technology, Vol.87, pp.56-63
- Publisher
- Elsevier
- Number of pages
- 8
- Grant note
- 51820105014 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC) DP140101547; LP150100413; DP170102211 / Australian Research Council
- Identifiers
- 991013125994702368
- Copyright
- © 2019 Elsevier Ltd. All rights reserved.
- Academic Unit
- Faculty of Science and Engineering
- Language
- English
- Resource Type
- Journal article