Chloride penetration depth (CPD) is a critical durability indicator for concrete structures, yet experimental data are often limited. This study evaluates whether increasing model complexity is beneficial under such constraints by comparing six machine learning and deep learning models—extreme gradient boosting, categorical boosting (CATB), random forest, multilayer perceptron (MLP), deep neural network (DNN), and a hybrid model combined with CATB and DNN (CatDNN)—using a dataset of 1078 cases. During training, CatDNN exhibited the earliest stabilization, reaching the best epoch at 40, while MLP and DNN stabilized after approximately 30 epochs. However, overfitting tracking revealed a flat tendency near 40 epochs for CatDNN, indicating potential data saturation. The test results showed small performance differences among all the models. CatDNN achieved the lowest max error (1.21), demonstrating effective residual correction, but its R2 (0.9123) was slightly lower than that of DNN (0.9129), suggesting that increased complexity did not yield meaningful improvement. The validation results confirmed high reliability across all the models (R2 ≥ 0.88). Overall, the findings indicate that, under limited data conditions, simple and well-fitted models can outperform or match complex hybrid architectures, emphasizing the importance of model efficiency over structural complexity.
Jang et al. (Mon,) studied this question.