Integration of Bayesian network with fuzzy analytical hierarchy process for determining the pipeline conditions

Nurul Sa aadah Sulaiman*, Henry Tan, Libriati Zardasti, Norhazilan Md Noor

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


The evaluation of subsea pipeline conditions and the calculation of the likelihood of failures are among the important factors for effective maintenance decision-making. Traditional qualitative methods to calculate the likelihood of failures are subjective, highly dependent on the experience and knowledge of the decision-makers, and suffer data limitations. Thus, the calculated likelihood of failures may not reflect the actual value, resulting in an improper maintenance program. In this work, an analysis of subsea pipeline conditions based on a Bayesian Network was proposed to handle knowledge uncertainties and assist in decision-making. This work aims to elucidate the conversion of experts' perceptions into a pseudo-quantitative likelihood for conditional probability tables (CPTs) elicitation for the proposed Bayesian network. The experts' opinion was transformed into a more crisp value to be integrated with the objective data for accurate determination of the failure likelihood. The formulation to predict the likelihood of pipeline failures that relies on experts' perceptions was developed using the artificial intelligent fuzzy analytical hierarchy process (FAHP) with the decomposition method. The proposed pseudo-quantitative formulation was established and was able to complement the existing risk-based model, which enabled the making of more informed pipeline maintenance decisions. The approach assisted the experts in eliciting the probabilities of nodes with emphasis on generating the conditional probabilities of the nodes with multiple parents.

Original languageEnglish
JournalProcess Safety Progress
Early online date2 Mar 2022
Publication statusE-pub ahead of print - 2 Mar 2022


  • conditional probability table
  • decomposition method
  • fuzzy analytical hierarchy process
  • knowledge uncertainties
  • pseudo-quantitative likelihood


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