A Bayesian Approach to Identifying Structural Nonlinearity using Free-Decay Response: Application to Damage Detection in Composites

Abstract

This work discusses a Bayesian approach to approximating the distribution of parameters governing nonlinear structural systems. Specifically, we use a Markov Chain Monte Carlo method for sampling the posterior parameter distributions thus producing both point and interval estimates for parameters. The method is first used to identify both linear and nonlinear parameters in a multiple degree-of-freedom structural systems using free-decay vibrations. The approach is then applied to the problem of identifying the location, size, and depth of delamination in a model composite beam. The influence of additive Gaussian noise on the response data is explored with respect to the quality of the resulting parameter estimates.

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Document Details

Document Type
Technical Report
Publication Date
Mar 03, 2010
Accession Number
ADA515126

Entities

People

  • C. C. Olson
  • Jonathan M. Nichols
  • K. D. Murphy
  • W. A. Link

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Algorithms
  • Bayesian Networks
  • Composite Materials
  • Computational Science
  • Damage Detection
  • Data Science
  • Detection
  • Differential Equations
  • Equations
  • Information Science
  • Models
  • Monte Carlo Method
  • Nonlinear Systems
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Random Variables

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Structural Dynamics.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference