Bayesian Identification of a Cracked Plate using a Population-Based Markov Chain Monte Carlo Method

Abstract

Estimating damage in structural systems is a challenging problem due to the complexity of the likelihood function describing the observed data. From a Bayesian perspective a complicated likelihood means efficient sampling of the posterior distribution is difficult and standard Markov Chain Monte Carlo samplers may no longer be sufficient. This work describes a population-based Markov Chain Monte Carlo approach for efficient sampling of the damage parameter posterior distributions. The approach is shown to accurately estimate the state of damage in a cracked plate structure using simulated, free-decay response data. The use of this approach in identifying structural damage has not previously been explored.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2011
Accession Number
ADA543553

Entities

People

  • E. Z. Moore
  • Jonathan M. Nichols
  • K. D. Murphy

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Bayesian Networks
  • Computational Science
  • Crack Tips
  • Data Science
  • Evolutionary Algorithms
  • Genetic Algorithms
  • Identification
  • Information Science
  • Markov Chains
  • Modal Analysis
  • Monte Carlo Method
  • Probability
  • Probability Distributions
  • Random Variables
  • Sampling
  • Standards

Fields of Study

  • Mathematics

Readers

  • Facility/Structural Engineering.
  • Statistical inference.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference