ArticleMaterials (Basel, Switzerland)2026
Prediction of Chloride Penetration Depth in Concrete Using a Combined Ensemble-Neural Network Architecture: Facing Data Saturation.
Article in Materials (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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4 authors.
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Abstract
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 R
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