Deterministic and stochastic damage detection via dynamic response analysis
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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. Starting from a mathematical model of the acoustic wave, we calibrate its decisive parameters (wave speed and damping coefficient) by comparing measurements with simulations in a case study. 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.
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