Chloride-induced damage of coastal concrete structure leads to serious structural deterioration. Thus, chloride content in concrete is a crucial parameter for determining the corrosion state. This study aims at establishing machine learning models for chloride diffusion prediction with the utilizations of the Multi-Gene Genetic Programming (MGGP) and Multivariate Adaptive Regression Splines (MARS). MGGP and MARS are well-established methods to construct predictive modeling equations from experimental data. These modeling equations can be used to express the relationship between the chloride ion diffusion in concrete and its influencing factors. Moreover, a data set, which contains 132 cement mortar specimens, has been collected for this study to train and verify the machine learning approaches. The prediction results of MGGP and MARS are compared with those of the Artificial Neural Network and Least Squares Support Vector Regression. Notably, MARS demonstrates the best prediction performance with the Root Mean Squared Error (RMSE) = 0.70 and the coefficient of determination (R2) = 0.91.

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https://doi.org/10.1016/j.measurement.2017.08.031