Abstract
The elastic wave tomography method is widely used in the nondestructive testing of concrete structures. A critical problem still exist that the tomography results are highly sensitive to measurement errors, leading to significant uncertainty. However, this issue is generally overlooked and seldom evaluated. This study attempts to address this gap by proposing a novel approach that introduces the Bayesian method to quantify uncertainty in elastic wave tomography for concrete structures. First, measurement errors are modeled as a Gaussian distribution, and a multivariate Bayesian linear regression model is employed to describe the relationship of travel time and slowness. Then, the Gibbs sampler is applied to obtain samples from the parameters’ full conditional distribution. The proposed approach is found to be effective by both numerical simulation and experimental studies. Hence, it could be a potential method to identify defect positions while also quantifying the uncertainty of the elastic wave tomography results.
Published Version
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