Impact Load Reconstruction for Composite Structures Using a Gated Temporal Convolutional Network
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Techniques for identifying impact forces are crucial for improving the operational reliability and safety of aerospace and railway composite structures. To address the challenges in impact load reconstruction arising from the complex nature of composite materials, this paper proposes a novel data-driven method based on a Gated Temporal Convolutional Network (GTCN). The proposed framework establishes a direct inverse mapping from structural vibration responses, captured by a network of piezoelectric (PZT) sensors, to the impact force's time history. The GTCN architecture leverages dilated convolutions to exponentially expand the receptive field for capturing long-term dependencies in sensor signals, while a gating mechanism is employed to selectively filter critical features. The efficacy of the proposed method was validated through a comprehensive finite element simulation dataset, encompassing various impact scenarios on a T700/BA9916 CFRP laminate. Simulation results demonstrate that the GTCN method achieves robust impact force reconstruction with an average relative error of 5.8% across different impact types (Gaussian, half-sine, and sawtooth). The proposed method demonstrates considerable potential for application in advanced structural health monitoring (SHM).