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research-article

Time-Variant Ultimate Reliability Analysis of Jacket Platforms Considering a New Probabilistic Corrosion Model for the Persian Gulf

[+] Author and Article Information
Hossein Gholami

K.N. Toosi University of Technology, Civil Engineering Faculty, No. 1346, Vali Asr Street, Mirdamad Intersection, Tehran, Iran
hosein.gholami@gmail.com

Behrouz Asgarian

Professor, K.N. Toosi University of Technology, Civil Engineering Faculty, No. 1346, Vali Asr Street, Mirdamad Intersection, Tehran, Iran
asgarian@kntu.ac.ir

Saeed Asil Gharebaghi

Assistant Professor, K.N. Toosi University of Technology, Civil Engineering Faculty, No. 1346, Vali Asr Street, Mirdamad Intersection, Tehran, Iran
asil@kntu.ac.ir

1Corresponding author.

ASME doi:10.1115/1.4040505 History: Received November 17, 2017; Revised May 31, 2018

Abstract

Corrosion is identified as one of the most important deterioration factors for structural integrity of offshore platforms. For reliability analysis of these platforms, a probabilistic model for prediction of long-term corrosion loss as a function of time is essential. The purpose of this study is to propose a novel model for steel corrosion of jacket platforms in the Persian Gulf region. Field measurements for members in seawater are collected and statistically analyzed to identify the probability function for corrosion loss at different times. A new model with time-dependent parameters is suggested, based on the statistical analysis results. Application of above model in the reliability analysis of jacket platforms is investigated by introducing a new reliability analysis framework. This framework is a general solution for probabilistic analysis of jacket platforms with several stochastic variables, which can be used for the platforms with different configuration and loads. In this framework, direct analysis is performed in each stage of FORM instead of using the response surface method, which is a common approach to obtaining the required response. This framework is applied to three jackets and the annual probability of failure over the platforms service life is computed. Comparison of results revealed that among the years beyond the platform design life, the amount of annual P_f is increased in parabolic function. In addition, studying the results is illustrated that in the case of ignoring the corrosion loss as a stochastic variable, P_f is estimated 7% lower than values obtained in actual condition.

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