Bayesian Network inference for probabilistic strength estimation of aging pipeline systems

Sonam Dahire, Fraaz Tahir, Yang Jiao, Yongming Liu

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


A novel approach for reliability assessment of aging gas pipeline systems based on a Bayesian network methodology is proposed in this paper with a focus on the improvement of the pipeline strength prediction. A multimodal diagnosis is performed by assessing the variation in the mechanical property (e.g., yield strength) within the pipe in terms of material property measurements, such as microstructure, composition, and hardness. The multimodality measurements are then integrated with the Bayesian network information fusion model. Prototype testing is carried out for model verification, validation and demonstration. The model updating scheme employs a Markov Chain Monte Carlo algorithm to infer the posterior distribution of the pipe strength using the multimodality measurements, whereas, the priors are derived from the literature knowledge of such systems. Moreover, through-thickness studies of pipe cross-sections are performed to demonstrate the mechanical property variation from the surface to bulk. Finally, data training of the model is employed to obtain a more accurate measure of the probabilistic pipe strength. Discussions on the observations and future work are provided.

Original languageEnglish (US)
Pages (from-to)30-39
Number of pages10
JournalInternational Journal of Pressure Vessels and Piping
StatePublished - May 2018


  • Bayesian Network
  • Inference
  • Multimodal diagnosis
  • Probabilistic
  • Strength

ASJC Scopus subject areas

  • Materials Science(all)
  • Mechanics of Materials
  • Mechanical Engineering


Dive into the research topics of 'Bayesian Network inference for probabilistic strength estimation of aging pipeline systems'. Together they form a unique fingerprint.

Cite this