Cable Damage Identification of Long-Span Cable-Stayed Bridges Using Spatial-Attention-Enhanced Multilayer Perceptron

Stay cables are essential load-bearing elements of long-span cable-stayed bridges but are highly susceptible to degradation and fatigue. This study develops a deep learning framework for locating and quantifying stay cable damage. All training, validation, and independent test data in this study were generated by an OpenSees finite element model covering different cable damage locations and severities. A multilayer perceptron (MLP) classification model integrated with a spatial attention module (SAM) is constructed to achieve accurate localization of damaged stay cables. Based on this, a regression model with a similar architecture to the classification model is employed for damage quantification. In this identification procedure, SAM is adopted to enhance key spatial features, and MLP is used to separately train the Damage Location Identification Model (DLIM) and Damage Severity Identification Model (DSIM). After training, the identification accuracy of DLIM reaches 99.91%, and the identification reliability of DSIM reaches 99.95%. Under a signal-to-noise ratio (SNR) of 15 dB, the DLIM accuracy and DSIM reliability remain 94.76% and 97.33%. The framework provides a numerical case-study assessment of stay cable damage identification under the tested data protocol and Additive White Gaussian Noise (AWGN) condition.