Element Modal-Based Structural Damage Detection by Two-Dimensional Convolutional Neural Networks

Abstract

Convolutional neural networks (CNNs) have strong noise resistance, and this study utilizes this property to weaken the impact of noise on structural damage identification data. After structural damage occurs, the modal parameters at the unit level are particularly sensitive to changes in damage and can therefore be used as important characteristic indicators for identifying damage. This article establishes a finite element model of steel truss and introduces damage at different positions and degrees. The free vibration process of the structure is simulated by the finite element method (FEM), and the first-order modal characteristic parameters, including modal strain energy and modal strain, are extracted for each damage situation. Subsequently, these modal parameters and the corresponding damage information are input as training samples into the CNN model for automatic identification of structural damage. The results show that the constructed CNN model can accurately identify the location and degree of structural damage, with a damage localization accuracy of 100% and a relative error of only 6.6% for damage degree identification. Among various characteristic indicators, modal strain energy difference exhibits better sensitivity and stability. Compared with traditional backpropagation (BP) neural networks, the CNN shows improved detection accuracy, by about 35%, and computation time is only 2.4% of BP networks. In addition, the CNN maintains good recognition performance in low order modes, which is of great significance for easily obtainable measurement data in practical engineering. In summary, the CNN method shows superior performance in damage localization, damage degree recognition, and noise resistance and has high engineering application value.