Enhancing Vibration-based Damage Assessment with 1D-CNN: Parametric Studies and Field Applications

Deep learning approaches have emerged as promising solutions for vibration-based damage assessment. Although these approaches have shown great potential, further investigations are required to apply them to real-world problems, as most studies have been limited to training with experimental and numerical simulation data. To address this, this study examines the feasibility of employing a one-dimensional convolutional neural network (1D-CNN) for damage assessment by utilizing both simulated data and field applications on an actual truss bridge. Extensive parametric studies were conducted to investigate the performance of the model under various architectural configurations, sensor quantities, sensor locations, and degrees of damage. The results of the hyperparameter optimization show that a moderate number of optimizable parameters is essential for the universal applicability of optimized hyperparameters across different situations or configurations. Comparative studies with other machine learning and deep learning algorithms have validated the superior performance of the 1D-CNN in vibration-based damage detection. Finally, the field application demonstrated the robust potential of the 1D-CNN for real-world scenarios, achieving an impressive F1-score of 90.58% even with single-channel measurements.