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◆ Journal of Materials in Civil Engineering2026-01-24· Latin hypercube sampling

Experimental and Numerical Study of Fatigue Life Prediction for High-Strength Steel Wires Considering Corrosion Damage and Mean Stress

Zimo Zhang, Guojin Tan, Tao Yang, Hua Wang

原始摘要(英文原文)· Original abstract
High-strength steel wires used in bridge suspenders are highly susceptible to damage and fatigue failure caused by environmental corrosion and fatigue loading, seriously threatening bridge safety. S-N curves are widely used for fatigue life prediction, whereas existing models primarily focus on high-strength steel wires with minimal damage or specific corrosion rates and are inadequate for predicting wires with random or uncertain corrosion rates. Therefore, a new S-N curve considering corrosion damage and mean stress was developed to provide a convenient and accurate method for the fatigue life prediction of high-strength steel wires under unknown corrosion rates. First, the finite-element model of a damaged high-strength steel wire was established, considering the morphological characteristics of the pits induced by environmental corrosion. Second, Latin hypercube sampling was used to generate 100 sets of parameters, and the fatigue life of high-strength steel wires under varying parameter conditions was predicted using numerical simulations. Furthermore, an S-N curve integrating the damage parameters and mean stress was proposed based on these predictions. Finally, fatigue tests were conducted on damaged high-strength steel wires, showing a maximum deviation of 6.79% between the predicted fatigue life and test results. Accordingly, the proposed S-N curve provides an effective method for fatigue life prediction of corrosion-damaged high-strength steel wires in suspenders, which is helpful for structural health monitoring and optimization of maintenance strategies for bridges.
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Experimental and Numerical Study of Fatigue Life Prediction for High-Strength Steel Wires Considering Corrosion Damage and Mean Stress — 科研速览 Science Skim