Strain-Based Quantitative Inversion of Localized Corrosion Defects in Storage Tanks Using Finite Element-Driven Machine Learning

Wall thinning caused by corrosion changes the circumferential strain response of storage tanks, but nonlinear coupling among structural parameters, loading conditions, strain characteristics, and defect geometry makes direct inversion difficult. This paper develops a strain-based, finite element (FE)-based machine learning model to predict corrosion depth and pit diameter from externally observable strain features. A reduced-scale hydrostatic test verified the external observability of localized inner wall thinning. A parametric finite element model was then used to generate 1072 samples. TabNet was selected among five regression models optimized using Bayesian optimization (BO), and Multi-Head Attention (MHA) was incorporated to improve feature interactions. On a held-out test subset of the FE-generated dataset, the dual-output BO-MHA-TabNet achieved mean absolute errors (MAEs) of 0.226 mm for corrosion depth and 24.252 mm for pit diameter. The corresponding R2 values were 0.9612 and 0.9377, respectively. Shapley additive explanations (SHAP) analysis identified the strain concentration factor and maximum circumferential strain as the most significant features, consistent with local stiffness reduction and strain concentration. This study is a numerical proof of concept supported by an observability experiment. The proposed framework provides an interpretable approach for strain-based quantitative evaluation of corrosion defects in storage tanks.