Integrated approach to assessing strength in slag-based geopolymer mortars: experimental study and modeling with advanced techniques

Abstract

The study consists of two main parts. In the initial phase, a variety of slag-based geopolymer mortars with different activator concentrations were prepared. These mortars underwent curing in both water and air environments for periods of 3, 7, 28, and 90 days, after which their compressive strength was evaluated at the conclusion of each curing interval. The second phase of the study is dedicated to the development of innovative models for estimating the compressive strength based on the data gathered. To achieve this, a range of techniques including multi-gene genetic programming (MGGP), artificial neural networks (ANN), XGBoost, SVM-Gauss, long short-term memory (LSTM), and convolutional neural networks (CNN) were employed to formulate a model capable of estimating compressive strength accurately. The study made use of various performance evaluation metrics such as mean squared error (MSE), root mean squared error (RMSE), R-squared, mean absolute error (MAE), and scatter index (SI) to assess the precision of the MGGP method in evaluating slag-based geopolymer mortars under both water and air curing conditions. The findings indicate that the equations generated by the MGGP method exhibit a high level of precision when juxtaposed with experimental outcomes. This research endeavors to enhance the prediction of compressive strength in geopolymer mortars, a subject that has garnered significant interest in scholarly literature.