A data-driven model for damage evolution of bridge stay cable

Abstract

Chloride ion concentration is crucial for the reliability of stay cables, particularly when their protective covers are compromised. Previous studies have focused on the corrosion of single wires or cables over time, proposing estimation models that primarily consider corrosion duration while neglecting other significant factors. Addressing this gap, this paper establishes a corrosion damage model for stay cables considering time, temperature, humidity and inclination angle based on the diffusion characteristics of chloride ions. Firstly, We conducted accelerated corrosion experiments on stay cables in different environments to assess the characteristics of the spatial distribution of chloride ion concentrations within the cables and the mass loss characteristics of the wires. Secondly, a three-dimensional diffusion model for chloride ions in stay cables is proposed based on Fick's second law. A pivotal innovation of our research is the application of diverse machine learning techniques to explore how the diffusion coefficient of chloride ions is influenced by factors like time and environmental temperature, leading to the formulation of a sophisticated chloride ion diffusion model. Finally, by amalgamating this diffusion model with the established corrosion law for steel reinforcement, we devised a comprehensive corrosion damage evolution model for the steel wires of stay cables. The result demonstrates the anisotropic diffusion of chloride ions and its substantial correlation with the corrosion damage observed in steel wires. Notably, the PSO-BPNN method emerged as a highly accurate predictor for the diffusion coefficient of chloride ions. The model established in this paper considers a variety of influencing factors and accurately predicts the corrosion damage characteristics of stay cables, which makes an important contribution to improving the reliability of stay cables.