Predicting the strength of recycled glass powder-based geopolymers for improving mechanical behavior of clay soils using artificial intelligence

Abstract

Abstract. The paper investigates the use of artificial intelligence (AI) methods to predict the strength of recycled glass powder (RGP) and soil mixtures based on different input parameters. The study utilized a database of 57 sets with 5 inputs, including RGP percentage, ordinary Portland cement (OPC) percentage, molar concentration, curing temperature and time, and one output, mixed UCS. There were two artificial intelligence models used in this study, a support vector machines (SVM) and classification and regression random forest (CRRF). The results demonstrate the potential of RGP-based geopolymers to improve the mechanical behavior of clay soils, and the use of AI methods to predict the strength of RGP and soil mixtures with high accuracy. Using SVM model, the testing dataset had a mean absolute error (MAE) and R2 of 0.072 and 0.978, respectively. Also, CRRF had an accurate performance with a MAE of 0.075 and the R2 of 0.979. These results suggest that the AI models fits well with the data. Also, by analyzing the results of the SVM and CRRF models, it is found that curing time is the most important input parameter, while RGP and OPC are the least significant.