Chen Qian, Cai Yikun, Zhu Yuqin, Ji Haodi, Ma Xiaobing, Wang Han
School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China.
School of Aeronautics and Astronautics, Sichuan University, Chengdu 610065, China.
Materials (Basel). 2025 Aug 11;18(16):3760. doi: 10.3390/ma18163760.
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.
腐蚀是海洋钢中主要的失效机制，准确的腐蚀预测对于有效的维护和保护策略至关重要。然而，腐蚀数据集的有限可用性给预测模型的准确性和泛化带来了重大挑战。本研究介绍了一种为小样本量下的海洋腐蚀预测设计的新型集成模型。该模型将动态海洋环境因素和材料特性作为输入，腐蚀速率作为输出。首先，采用遗传算法（GA）优化的机器学习框架来推导最优的GA-XGBoost模型。为了进一步提高模型性能，提出了一种结合高斯混合模型和回归生成对抗网络（GMM-RegGAN）的虚拟样本生成方法。通过将这些生成的虚拟样本纳入基础模型，预测精度得到进一步提高。使用六种海洋钢的腐蚀数据集对所提出的框架进行了验证。结果表明，GA优化显著提高了模型的性能和稳定性。虚拟样本生成进一步提高了预测性能，RMSE降低了14.94%，MAE降低了15.55%，MAPE降低了14.04%。结果表明，所提出的方法为样本数据有限的情况下的腐蚀预测提供了一个强大而有效的框架。
