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The development of posterior probability models in risk-based integrity modeling

Citation

Thodi, PN and Khan, FI and Haddara, MR, The development of posterior probability models in risk-based integrity modeling, Risk Analysis, 30, (3) pp. 400-420. ISSN 0272-4332 (2010) [Refereed Article]

DOI: doi:10.1111/j.1539-6924.2009.01352.x

Abstract

There is a need for accurate modeling of mechanisms causing material degradation of equipment in process installation, to ensure safety and reliability of the equipment. Degradation mechanisms are stochastic processes. They can be best described using risk-based approaches. Risk-based integrity assessment quantifies the level of risk to which the individual components are subjected and provides means to mitigate them in a safe and cost-effective manner. The uncertainty and variability in structural degradations can be best modeled by probability distributions. Prior probability models provide initial description of the degradation mechanisms. As more inspection data become available, these prior probability models can be revised to obtain posterior probability models, which represent the current system and can be used to predict future failures. In this article, a rejection sampling-based Metropolis-Hastings (M-H) algorithm is used to develop posterior distributions. The M-H algorithm is a Markov chain Monte Carlo algorithm used to generate a sequence of posterior samples without actually knowing the normalizing constant. Ignoring the transient samples in the generated Markov chain, the steady state samples are rejected or accepted based on an acceptance criterion. To validate the estimated parameters of posterior models, analytical Laplace approximation method is used to compute the integrals involved in the posterior function. Results of the M-H algorithm and Laplace approximations are compared with conjugate pair estimations of known prior and likelihood combinations. The M-H algorithm provides better results and hence it is used for posterior development of the selected priors for corrosion and cracking. © 2010 Society for Risk Analysis.

Item Details

Item Type:Refereed Article
Keywords:Asset integrity; Bayes's theorem; Corrosion; Cracking; Posterior; Prior; Acceptance criteria; Accurate modeling; Conjugate pair; Corrosion cracking; Current system; Degradation mechanism; Estimated parameter; H-algorithm; In-process; Markov Chain
Research Division:Engineering
Research Group:Maritime Engineering
Research Field:Ocean Engineering
Objective Division:Mineral Resources (excl. Energy Resources)
Objective Group:Environmentally Sustainable Mineral Resource Activities
Objective Field:Environmentally Sustainable Mineral Resource Activities not elsewhere classified
Author:Khan, FI (Professor Faisal Khan)
ID Code:94539
Year Published:2010
Web of Science® Times Cited:8
Deposited By:NC Maritime Engineering and Hydrodynamics
Deposited On:2014-09-11
Last Modified:2014-09-11
Downloads:0

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