High-precision Lamb wave evaluation for corrosion damage with model-based Bayesian Optimization and Gaussian meta-modeling strategy

Abstract

The presence of corrosion damage in metallic structures is a critical problem affecting system safety, which poses challenges for structural maintenance. Ultrasonic Lamb wave testing has shown many benefits, including high sensitivity and a large coverage area. However, multiple external factors are causing a mismatch between the imaging result and the actual damage. This study proposes a Bayesian Optimization-based model calibration framework, incorporating a Gaussian process (GP) measurement model, for accurate corrosion damage quantification. In this method, one critical problem is obtaining the high-fidelity Finite Element Method (FEM) model. The initial FEM model usually has inevitable modeling errors due to necessary simplifications and idealized constraints. Here we introduced Bayesian Optimization by which the numerical model could be calibrated. The calibrated FEM model, which had fewer discrepancies with actual experiments, could generate high-fidelity signals corresponding to various damage severity. Then, the GP measurement model outputted the mean and variance of the corrosion width and depth corresponding to the differences between experiment signals and model outputs. The inversion of damage information from the established GP model was accomplished by the genetic algorithm. Hence, both the damage size and depth could be evaluated with high accuracy. Once the corrosion information along different propagation paths was obtained, it can be combined with the location of the corrosion damage center for locating the edge points of the corrosion damage. In the end, the edge points were fitted using the least square algorithm. Validations were experimentally performed, and the detection result showed good consistency with the actual damage.