(8)Cost consequence-based reliability analysis of bursting and buckling failure modes in subsea pipelines
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Bahram Mehrafrooz a, Pedram Edalat a,∗, Mojtaba Dyanati b
a Offshore Structural Engineering Department, Petroleum University of Technology, Mazandaran, Iran
b The University of Akron, United States
Received 5 June 2018; received in revised form 1 January 2019; accepted 2 January 2019
Available online 4 January 2019
Abstract
Risk assessment is a fundamental activity to evaluate the integrity of pipeline systems during their life cycle, which allows pipeline operators to focus attention on integrity management activities in order to prevent and mitigate failure and maintain the design safety level. Risks for materials and contractor’s quality are a major source of concern about the integrity of pipelines and have a remarkable influence on the optimized balance between capital expenditure (CAPEX) and operation expenses (OPEX) in risk-based integrity management of pipeline. While other sources of risks to pipelines have been investigated thoroughly, the impacts of construction and material quality have not been well studied yet. This paper addresses the influence of construction accuracy/quality and corresponding uncertainties on the failure of submarine pipelines in the presence of corrosion. Corrosion-bursting and corrosion-buckling failure modes (which are addressed as ultimate limit states of subsea pipeline in DNV-OS-F101) are investigated in a reliability analysis to predict critical failure year of the pipe. Sensitivity analysis is also implemented to estimate the importance of parameters on failure probability using Monte Carlo Simulation (MCS). The results show a high sensitivity of failure probability to construction accuracy, which defines contractor quality.
© 2019 Shanghai Jiaotong University. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license. (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Keywords: Subsea pipeline; Bursting; Buckling; Construction quality; Reliability; Monte Carlo Simulation.