Corrosion fatigue crack growth prediction of bridge suspender wires using Bayesian gaussian process

This paper proposes a Bayesian gaussian process-based corrosion fatigue damage assessment framework for bridge suspender wires. Prior statistical parameters in the fatigue life prediction model are obtained by corrosion fatigue crack growth test under different stress ratios. A numerical simulation method of corrosion fatigue crack growth is developed combined with ABAQUS and FRANC3D. Following this, a Bayesian network-based frame-work is established by integrating fatigue crack growth model, stress concentration factor and observed fatigue crack length information to reduce the uncertainties. The uncertainties exist in stress concentration factor are quantified by gaussian process regression-based surrogate model, and the pit geometry of length, width and depth are selected as three-dimensional input. Markov Chain Monte Carlo simulation is used to obtain the posterior distribution of parameters in the Bayesian network. The results show that the fatigue crack growth predictions agree well with experimental observations. The proposed method can simplify the complicated modeling process of stress concentration factor under the influence of corrosion pit, and can effectively update the model parameters to accurately predict the corrosion fatigue crack of bridge suspender wires.