Prediction of compressive strength and feature importance analysis of solid waste alkali-activated cementitious materials based on machine learning

Abstract

This study examines the analysis and treatment of diverse types of solid waste alkaline-activated cementitious materials utilizing machine learning techniques. To account for the variability of solid waste alkaline-activated cementitious materials, three crucial chemical components (aluminum trioxide, calcium oxide, and silicon dioxide) were chosen as representative features. A total of 274 pertinent datasets were collected and split into training and testing sets with an 80% to 20% ratio for analysis. Six machine learning models, including multilayer feedforward neural network, genetic algorithm neural network, support vector machine, random forest, radial basis function neural network, and long short-term memory network, were employed to construct predictive models. The optimal hyperparameters were identified via grid search. The performance of the models was assessed using the training and testing sets, revealing that all models exhibited strong predictive and generalization capabilities on both sets. Among the models, the support vector machine model achieved the highest performance, yielding an R2 value of 0.9054, MAE of 4.1460, and NRMSE of 0.0997. Through the utilization of SHAP (Shapley Additive Explanations) analysis, the interplay between factors and the significance of features were examined. Calcium oxide, water-to-binder ratio, silicon dioxide, modulus of water glass, and aluminum trioxide were recognized as the primary factors that influence the compressive strength. The research findings offer guidance for optimizing the performance of alkaline-activated cementitious materials made from solid waste.