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Ultrasonic guided wave technology has played a significant role in the fieldof non-destructive testing as it employs acoustic waves that have advantages ofhigh propagation efficiency and low energy consumption during the inspectprocess. However, theoretical solutions to guided wave scattering problemsusing assumptions such as Born approximation, have led to the poor quality ofthe reconstructed results. To address this issue, a novel approach toquantitative reconstruction of defects using the integration of data-drivenmethod with the guided wave scattering analysis has been proposed in thispaper. Based on the geometrical information of defects and initial results bythe theoretical analysis of defect reconstructions, a deep learning neuralnetwork model is built to reveal the physical relationship between defects andthe received signals. This data-driven model is then applied to quantitativelyassess and characterize defect profiles in structures, reduce the inaccuracy ofthe theoretical modelling and eliminate the impact of noise pollution in theprocess of inspection. To demonstrate advantages of the developed approach toreconstructions of defects with complex profiles, numerical examples includingbasic defect profiles and a defect with the noisy fringe have been examined.Results show that this approach has greater accuracy for reconstruction ofdefects in structures as compared with the analytical method and provides avaluable insight into the development of artificial intelligence-assistedinspection systems with high accuracy and efficiency in the field ofnon-destructive testing.

A novel combination of theoretical analysis and data-driven method for reconstruction of structural defects