Bayesian information fusion of multmodality nondestructive measurements for probabilistic mechanical property estimation

Jie Chen, Yongming Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Missing data occur when no data value is available for the variable in an observation. In this research, Bayesian data augmentation method is adopted and implemented for prediction with missing data. The data augmentation process is conducted through Bayesian inference with missing data assuming the multivariate normal distribution. Gibbs sampling is used to draw posterior simulations of the joint distribution of unknown parameters and unobserved quantities. The missing elements of the data are sampled conditional on the observed elements. The distribution of model parameters and variables with missing data can be obtained for reliability analysis. Two examples are given to illustrate the engineering application of Bayesian inference with missing data. The first example is to predict the yield strength of aging pipeline by fusing the incomplete surface information with missing data. The predictive performance is compared among direct surface indentation technique, linear regression with complete data and Bayesian inference with missing data. The second example is to predict the fatigue life of corroded steel reinforcing bar from the incomplete input dataset. The predicted fatigue lives are compared with experimental data. Both examples demonstrate that the Bayesian method can deal with missing data problem properly and show good predictive performance.

Original languageEnglish (US)
Title of host publicationSafety Engineering, Risk, and Reliability Analysis
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791884669
DOIs
StatePublished - 2020
Externally publishedYes
EventASME 2020 International Mechanical Engineering Congress and Exposition, IMECE 2020 - Virtual, Online
Duration: Nov 16 2020Nov 19 2020

Publication series

NameASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
Volume14

Conference

ConferenceASME 2020 International Mechanical Engineering Congress and Exposition, IMECE 2020
CityVirtual, Online
Period11/16/2011/19/20

Keywords

  • Bayesian inference
  • Corrosion fatigue
  • Missing data
  • Yield strength

ASJC Scopus subject areas

  • Mechanical Engineering

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