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Deterministic and stochastic damage detection via dynamic response analysis
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DOI：10.1016/j.ijar.2020.08.008.png)
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The paper proposes a method of damage detection in elastic materials, which is based on analyzing the time-dependent (dynamic) response of the material excited by an acoustic signal. A case study is presented consisting of experimental measurements and their mathematical analysis. The decisive parameters (wave speed and damping coefficient) of a mathematical model of the acoustic wave are calibrated by comparing the measurement data with the numerically evaluated exact solution predicted by the mathematical model. The calibration is done both deterministically by minimizing the square error over time and stochastically by a Bayesian approach, implemented through the Metropolis-Hastings algorithm. The resulting posterior distribution of the parameters can be used to construct a Bayesian test for damage. (C) 2020 Elsevier Inc. All rights reserved.
Keyword:
Deterministic and stochastic parameter calibration
Wave propagation in elastic solids
Markov Chain Monte Carlo
Bayesian inference
Damage detection
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