Bayesian identification of a cracked plate using a population-based Markov Chain Monte Carlo method

Authors:
J. M. Nichols;E. Z. Moore;K. D. Murphy
Affiliations:
US Naval Research Laboratory, 4555 Overlook Ave., Washington, DC 20375, United States;University of Connecticut, Dept. of Mechanical Engineering, Storrs, CT 06269-3139, United States;University of Connecticut, Dept. of Mechanical Engineering, Storrs, CT 06269-3139, United States
Venue:
Computers and Structures
Year:
2011

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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.