Objective This paper aims to investigate the residual vertical bending strength of large open-box girders under random pitting corrosion. A stochastic pitting corrosion modeling method based on empirical data is proposed, and the impact of corrosion on the ultimate strength of structures is evaluated through nonlinear finite element analysis.

Methods To address this, a depth distribution model of pitting corrosion based on empirical pitting depth data from hull plates is constructed, and a corresponding numerical model is developed to assess the influence on the residual ultimate strength of corroded large-open box girders. In this study, a secondary development in Python within ABAQUS is employed to automate the generation of the pitting depth distribution model and the numerical simulation analysis. Nonlinear finite element analysis is performed to evaluate the influence of various corrosion parameters on the ultimate strength of the box girder.

Results The results indicate that, under the same corrosion volume loss, the effect of pitting radius on ultimate strength is minimal, with a difference of only 0.89% between the maximum and minimum average values. As the relative pitting area and relative pitting depth increase, the ultimate strength reduction factor shows a linear decreasing trend, with minimum values of 0.83 and 0.85, respectively. Additionally, When the pitting depth follows a Weibull distribution, the ultimate strength of the box girder decreases by a maximum of 16.7% under hogging conditions and 12.6% under sagging conditions. Finally, based on extensive numerical simulations, an empirical formula for predicting the residual ultimate strength of large open-box girders under vertical bending loads, considering random pitting loss, is also proposed.

Conclusions The methods presented in this paper provide significant references for assessing the residual ultimate strength of aging hull structures under vertical bending moments, demonstrating strong practicality and potential for broader application.