State Prediction Model of Steel Structure Based on VMD-CNN Method

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With the rapid pace of economic and social development, the complexity and diversity of building structures continue to evolve. Over their operational lifespan, structures are subjected to various forms of degradation, including material aging and environmental erosion, which can significantly diminish their durability. Consequently, the development of robust structural health monitoring systems becomes imperative. These systems not only track the evolving performance trends of structures but also predict potential failures, extending their operational lifespan and ensuring the safety of occupants and assets. This study addresses the challenges inherent in extracting detailed structural feature information and enhancing the accuracy of predictive models. Focused on the cantilever beam test model as a research framework, the research explores an innovative approach to structural state prediction. It integrates Variational Mode Decomposition (VMD) and Convolutional Neural Network (CNN) methodologies to effectively analyze and forecast structural conditions. The study highlights a significant improvement in prediction accuracy and overall model performance by comparing CNN’s predictions using original structural signals with those processed through VMD. The results show that when the number of modes [Formula: see text], compared with the original signal (when [Formula: see text]), the growth rate of the [Formula: see text]-value at each measuring point can reach a maximum of 288.13%, and the average growth rate is 101.07%. This indicates that the VMD-CNN can significantly improve the prediction performance of the model.