Abstract
Structural health monitoring and damage identification are essential for ensuring the safety and performance of engineering systems. Cracks introduce nonlinear dynamic behavior due to intermittent contact from the opening and closing of crack surfaces, which limits the effectiveness of conventional linear identification methods. Moreover, many existing approaches rely on multiple distributed sensors, which may be impractical in real-world applications. To address these limitations, this study investigates the feasibility of identifying both crack depth and location using single-point vibration measurements. A recently developed nonlinear analysis framework is employed to simulate the dynamic response of a cracked beam, and spectrograms of the tip response under various crack conditions are generated using the short-time Fourier transform. These spectrograms are then used to train a convolutional neural network for damage identification. Numerical results demonstrate that the proposed method achieves high coefficients of determination ([Formula: see text]) between the true and identified values for both crack depth and location, provided the training data sufficiently cover damage conditions within the defined parameter ranges. Furthermore, data augmentation is shown to enhance identification accuracy, underscoring the method's potential for implementation with limited vibration measurements.