Corrosion fatigue (CF) failure is one of the typical failure modes of high-strength steel wires for bridge cables because the cables are subjected to long-term fatigue loads and exposed to heavily polluted environment simultaneously. In this paper, a numerical simulation method was proposed to study CF performance of corroded high-strength steel wires. Firstly, the cellular automata (CA) method was used to generate a numerical model of corroded steel wires with corrosion pit, which can accurately describe the electrochemical process of metal corrosion. In the established CA model, three kinds of cells were involved, namely, metal cell, passive film cell, and corrosive medium cell. By setting 10 cellular transformation rules, morphology of the random corrosion pit on the steel wire surface was simulated. And then, a damage evolution model related to coupling of corrosivemedium and fatigue loads (CCF) was developed to describe the CF damage evolution process of steel wires. Subsequently, the damage evolution process was analyzed by ABAQUS with a user-defined material subroutine (UMAT). Finally, the life of corroded steel wires was predicted, and the CF performance of corroded steel wires with multiple corrosion pits was evaluated. The results show that the proposed method can reasonably describe the CF damage evolution process and illuminate the failure mechanism of steel wires subjected to the CCF. Damage of the steel wire with a single corrosion pit evolves gradually, and the damage evolution rate increases. For the steel wires with multiple corrosion pits, the corrosion pits affect mutually in the fracture process. When the angle and distance between corrosion pits reach a certain degree, the mutual effects can be ignored. With the same pit depth, the angle and distance among corrosion pits determine the CF life of steel wires mainly, and the number of corrosion pits affects slightly.
Corrigendum to “Corrosion fatigue failure analysis and service life prediction of high strength steel wire” [Eng. Fail. Anal. 110 (2020) 104440]
