Physics-Based Corrosion Reliability Analysis of Miter Gates Using Multi-scale Simulations and Adaptive Surrogate Modeling

Corrosion-induced crack initiation is primarily simulated in the meso-scale. Such physics-based simulation usually is computationally very expensive. It is computationally even more challenging, or impossible, if the meso-scale simulation model is coupled with macro-scale structural analysis for reliability analysis. This chapter breaks the computational barrier and makes it possible to perform physics-based corrosion reliability analysis of large structures using localized meso-scale simulations, by developing a novel adaptive surrogate modeling framework. A global surrogate model is first constructed at the macro-scale level to enable for the propagation of various input uncertainty sources, such as water levels and gap damage, to uncertainty of the stress response of the structure. After that, a local surrogate model is constructed to predict the local failure probability of any given location by accounting for uncertainty sources presented in both the macro- and meso-scale analysis models. To guarantee the accuracy of the local surrogate model and reduce the required number of meso-scale phase-field (PF) simulations for corrosion reliability analysis, an adaptive surrogate modeling method is proposed based on importance sampling (IS) and active learning to adaptively refine the surrogate model in critical regions. Corrosion reliability analysis of a miter gate structure is employed to demonstrate the efficacy of the proposed method. The result shows that the proposed framework can efficiently and accurately generate a failure probability map for a large structure like miter gate based on computationally expensive meso-scale PF simulations.