Prediction of Chloride Diffusion Coefficient in Concrete Based on Machine Learning and Virtual Sample Algorithm

• Published in: Sustainability Vol. 15; no. 24; p. 16896
• Main Authors: Zhou, Fei-Yu, Tao, Ning-Jing, Zhang, Yu-Rong, Yuan, Wei-Bin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2023
• Subjects: Accuracy; Algorithms; Artificial intelligence; Chloride; Concrete; Corrosion; Energy consumption; Machine learning; Neural networks; Neurons; Porous materials; Reinforced concrete; Support vector machines; Variables
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su152416896

The durability degradation of reinforced concrete was mainly caused by chloride ingress. Former studies have used component parameters of concrete to predict chloride diffusion by machine learning (ML), but the relationship between microstructure and macroparameter of concrete need to be further clarified. In this study, multi-layer perceptron (MLP) and support vector machine (SVM) were used to establish the prediction model for chloride diffusion coefficient in concrete, especially for the solid waste concrete. A database of concrete pore parameters and chloride diffusion coefficients was generated by the algorithm based on the Gaussian mixture model (GMM-VSG). It is shown that both MLP and SVM could make good predictions, in which the data using the normalization preprocessing method was more suitable for the MLP model, and the data using the standardization preprocessing method was more adapted to the SVM model.