Prediction of free chloride concentration in fly ash concrete by machine learning methods

Abstract

Free chloride concentration distribution is important for assessing the corrosion risk of steel bars in reinforced concrete structures under chloride environment. In this study, a group of 3150 free chloride concentration data sets were obtained. Afterwards, three machine learning methods, including Support Vector Regression (SVR), Multilayer Perceptron (MLP) and One-Dimensional Convolutional Neural Network (1D-CNN) were adopted to construct models to predict chloride concentration distribution. Results show that 1D-CNN and MLP models are better at predicting the chloride concentration in fly ash concrete, whereas the prediction capability of SVR is relatively poor. Moreover, free chloride concentration prediction based on unmeasured parameters was conducted. Results show that the 1D-CNN and MLP models both have high prediction abilities, i.e., predicted results are consistent with experimental measurements, performing generally better than the time-varying model constructed based on Fick's second law. When the free chloride concentrations were higher than 0.1%, the SVR model had a better prediction effect, but had an unsatisfactory result and differed significantly from the actual chloride concentration when at a lower concentration. Overall, the 1D-CNN model performs the best in predicting free chloride concentrations of concrete at different penetration depths, exposure time and with different FA content.