A CLASSIFICATION ALGORITHM FOR THE VORTEX-INDUCED VIBRATION DEVELOPMENT PROCESS BASED ON CONTINUOUS WAVELET TRANSFORM AND A CONVOLUTIONAL NEURAL NETWORK

Large-amplitude vortex-induced vibration (VIV) in long-span bridges can compromise safety and accelerate structural fatigue, underscoring the need for real-time monitoring and suppression. This study introduces a Classification algorithm for the VIV Development Process (CVIVDP), which monitors vibration states and issues early warnings via a trained classification model. The CVIVDP framework involves four steps: (1) categorizing the VIV process into five classes and annotating vertical response signals under various wind velocities; (2) converting VIV signals into RGB images using continuous wavelet transform (CWT) to reveal latent features through color-based geometric patterns; (3) training a GoogLeNet-based classifier adapted for the five-class task; and (4) outputting warnings based on classified real-world signals. Experimental results show that CVIVDP achieves precision, recall, and F1-scores of 92.86%, 94.44%, and 92.94%, respectively, outperforming existing methods. The proposed approach offers a viable solution for IoT-enabled bridge health monitoring.