Critical infrastructure integrity and reliability, from composites to high-voltage feeder pipes and railway tracks, demands precise, robust, and explainable nondestructive evaluation (NDE) techniques. In this thesis, the growing need for accurate damage detection, localization, and characterization under conditions involving high-speed inspections, varying sensor positions, and electromagnetic interference is addressed through a suite of data-driven, physics-informed, and domain-adaptive methodologies. The proposed framework integrates deep learning, advanced signal processing, and multi-fidelity modeling to address key challenges such as limited and unbalanced experimental datasets, mismatched simulation-to-field conditions, and signal distortion from environmental noise. First, a novel hybrid deep learning architecture is introduced that combines Generative Adversarial Networks (GANs) with Inception-based neural networks for coordinate-based Acoustic Emission (AE) source localization. This method achieves significant reductions in localization estimation errors, enabling reliable single-sensor monitoring. Complementing this effort, explainable deep convolutional neural network models are proposed for AE signal classification. Guided by physics-informed signal segmentation and advanced visualization techniques such as Class Activation Mapping (CAM) and Gradient-weighted CAM (Grad-CAM), these models illuminate the underlying mechanisms of Lamb wave mode interactions, thereby instilling trust and interpretability into the machine learning pipeline. Domain adaptation and transfer learning are central to this work. Specifically, the gap between abundant simulated data (source domain) and limited experimental measurements (target domain) is bridged to ensure that feature representations learned from large-scale synthetic datasets can be effectively transferred and fine-tuned. By integrating physics-informed constraints and knowledge transfer, the resulting models exhibit higher accuracy, are less prone to overfitting, and maintain interpretability in varied scenarios. A multi-fidelity Gaussian Process Regression (GPR) strategy is further presented for motion-induced eddy current testing (MIECT) to manage both forward (signal prediction) and inverse (defect estimation) problems under in-service inspection, sensor motion, and environmental noise. These GPR-based surrogates fuse low-fidelity finite element simulations with high-fidelity experimental data, accurately predicting sensor responses at inspection speeds exceeding typical laboratory conditions and enabling robust inverse estimations of defect geometries. In parallel, a novel auto-compensation algorithm for Pulsed Eddy Current (PEC) inspections is developed to address electromagnetic interference in feeder lines carrying high currents, significantly improving thickness estimation accuracy and reducing false-positive indications in underground pipeline applications.
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