Intelligent Structural Defect Reconstruction Using the Fusion of Multi-Frequency and Multi-Mode Acoustic Data

Quantitative detection of defects in structures is always a hot research topic the field guided wave inverse scattering. Research studies on how to effectively extract defect-related information encompassed multi-frequency and multi-modes scattered signals for reconstructions have been paid attention recent decades. In this paper, novel deep learning-based quantitative scattering technique has proposed intelligently realize end-to-end mapping multi-frequency, defect profiles with high levels accuracy efficiency. Based manifold distribution principle, data patterns SH-wave investigated, owing leveraging capability intelligent encoder-projection-decoder neural network. Following that, manifold-learning oriented network trained using generated by modified boundary element method. Several numerical examples examined demonstrate correctness efficiency reconstruction approach. It concluded that data-driven enables high-quality solution problems provides valuable insight into development practical approaches multi-modal acoustic from ultrasonic waves.