Prediction of Pore Solution Concentration in Cement Composite System by Using Machine Learning Techniques

Abstract

A thorough understanding of the pore solution's composition is crucial for a number of cementitious material properties, including durability. The pore solution concentration is determined by a variety of experimental techniques. However, these approaches aren't always straightforward. A possible substitute to complex pore solution extraction and analysis procedures could be machine learning (ML) models. The objective of this research is to explore ML techniques for predicting the cement pore solution composition composite systems produced with Ordinary Portland cement (OPC) and supplemental cementitious materials (SCM). Data on the compositions of pore solutions for different cementitious systems were gathered from the literature and combined into a comprehensive database that has over 400 data entries. Random Forest and Gradient Boosting techniques were applied to the database. Statistic metrics such as R2, RMSE and MAE were used to evaluate the prediction accuracy of the built model. Sensitivity analysis of the built models was carried out and compared. The gradient boosting technique was found to be the most effective method in prediction of the pore solution concentration (R2 ranging from (0.80–0.98) and lower RMSE values) due to its effective problem-solving capacity and minimum requirement for future engineering. Thus, ML models offer a potential approach for determining the pore solution concentration. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.