Damage identification analysis of Cable-stayed arch-truss based on multi-node time-domain data fusion

The potential risk of cable-stayed arch-truss damage is large and the damage is undetectable. The damage identification methods based on frequency domain have limitations such as limited data and complex theoretical methods. A damage identification method based on multi-node time-domain data fusion was proposed to overcome these limitations. The time-domain data library was established by finite element analysis, and the time-domain data was preprocessed and augmented. Two CNNs models were established to identify the damage location and damage degree of cable-stayed arch-truss. The proposed method was verified by the analysis of a practical cable-stayed arch-truss scale model, and the recognition effect of the method on noisy data and noise-free data was studied respectively. The results showed that the CNN can effectively identify the damage degree and damage location of cable-stayed arch-truss structure with good robustness. CNN with Gaussian noise can accurately predict the damage degree of cable-stayed arch-truss. The prediction error of most elements is within 15%, which can meet the actual needs of engineering.

Automated summary

This article proposes a damage identification method based on multi-node time-domain data fusion, and establishes a time-domain data library through finite element analysis. Two CNN models are used for identifying the damage location and degree of the cable-stayed arch-truss. The proposed method is verified through the analysis of a practical scale model, and the recognition effect on noisy and noise-free data is studied. The results show that CNN can effectively identify the damage degree and location with good robustness, and accurately predict the damage degree of the cable-stayed arch-truss with Gaussian noise within 15% error, meeting the engineering needs.