Prediction of Chloride Concentration in Fly Ash Concrete Based on Deep Learning

To explore the application of deep learning theory on predicting chloride concentration, 3 150 groups of free chloride concentration data were obtained by a long-term exposure test of fly ash concrete under natural tidal environment. A multi-layer perceptron (MLP) model with different activation functions and hidden layers, including four input parameters, namely water-cement ratio, exposure time, fly ash content and penetration depth, was established to predict the free chloride concentration in fly ash concrete. Results show that the prediction result is the best when the MLP model is constructed by using ReLu function with four hidden layers. Meanwhile, the selected optimal MLP model has more accurate precision when predicting the free chloride concentration based on the not measured parameters, compared with the method based on Fick's second law. Therefore, the MLP model has the advantages of high precision and wide application scope, which can be used as a new method for predicting free chloride concentration of concrete under chloride environment.