Corrosion is the predominant failure mechanism in marine steel, and accurate corrosion prediction is essential for effective maintenance and protection strategies. However, the limited availability of corrosion datasets poses significant challenges to the accuracy and generalization of prediction models. This study introduces a novel integrated model designed for predicting marine corrosion under small sample sizes. The model utilizes dynamic marine environmental factors and material properties as inputs, with the corrosion rate as the output. Initially, a genetic algorithm (GA)-optimized machine learning framework is employed to derive the optimal GA-XGBoost model. To further enhance model performance, a virtual sample generation method combining Gaussian Mixture Model and Regression Generative Adversarial Network (GMM-RegGAN) is proposed. By incorporating these generated virtual samples into the base model, the prediction accuracy is further improved. The proposed framework is validated using corrosion datasets from six types of marine steel. Results demonstrate that GA optimization substantially improves both the performance and stability of the model. Virtual sample generation further enhances predictive performance, with reductions of 14.94% in RMSE, 15.55% in MAE, and 14.04% in MAPE. The results indicate that the proposed method offers a robust and effective framework for corrosion prediction in scenarios with limited sample data.

Strain sensor placement strongly influences the accuracy and cost of deformation reconstruction with the inverse finite element method (iFEM). This study develops a common sensor layout for several loading regimes by coupling the four-node inverse shell element with a Lichtenberg-inspired local search embedded in the Non-dominated Sorting Genetic Algorithm III (LA-NSGA-III). At a prescribed sensor count, the layout is optimized by simultaneously minimizing reconstruction residuals for multiple training loads. The method is assessed using DTLZ benchmarks and a plate example under equal objective-evaluation budgets, with separate controls for the multi-load formulation and the local search operator. Across 20 paired plate runs with 23 sensor sites, the proposed search reduces the mean joint training residual by 18.65% relative to NSGA-III, with mean displacement errors of 0.73% and 0.77% for the two training loads. Additional pressure, torsional and off-center force cases, a withheld combined load, strain noise, and sensor loss are used to assess a fixed layout under changing conditions. A cantilever plate test demonstrates strain-based displacement reconstruction, with laser peak displacement measurements used to calibrate the FE reference model. The method supports the offline design of a single sensor layout for iFEM reconstruction under multiple anticipated loading conditions, reducing the need to reposition sensors when the load changes.