University of Tasmania

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Reliability assessment of offshore asset under pitting corrosion using Bayesian Network

Corrosion is a major cause of structural deterioration in marine and offshore industries. It affects the life of process equipment and pipelines resulting in structural failure, leakage, product loss, environmental pollution and the loss of life. Pitting corrosion is regarded as one of the most hazardous forms of corrosion in marine and offshore structures. Hence reliability assessment of these structures are crucial. The empirical and statistical degradation models are developed by either fitting field or lab data. However, these models are only useful for specific site or operating conditions and still carry a high degree of uncertainty. Other modeling approaches used for assessing rate of pitting corrosion in industry is phenomenological model which is based on corrosion scientific principles. These models provide strong understanding of corrosion process but are often hard to test in engineering applications. This paper presents a novel methodology for predicting the pitting corrosion rate of structural steel in long-term marine environment. The proposed methodology combines a multi-phase phenomenological and empirical model with calibrated real-world data using the Bayesian Network (BN) approach. A case study is presented which exemplifies the application of this methodology to predict the long-term pitting corrosion rate in marine environment. The result shows that the proposed BN based methodology is successful in predicting the time-dependent pitting corrosion rate for steel structures in different environmental conditions.


Publication title

Proceedings of Corrosion 2016




Australian Maritime College


NACE International

Place of publication

United States of America

Event title

Corrosion 2016

Event Venue

Vancouver, Canada

Date of Event (Start Date)


Date of Event (End Date)


Rights statement

Copyright 2016 NACE International

Repository Status

  • Restricted

Socio-economic Objectives

Expanding knowledge in engineering

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