Computational Intelligence for Modeling the Rheological Properties of the Developed Hydrated Lime-Based Alkali-Activated Materials

Abstract

The development of alkali-activated materials (AAMs) has gained attention as a sustainable alternative to Portland cement-based concrete. A key factor in AAM performance is its rheological behavior, which influences workability and pumpability. Accurate prediction of these properties is vital for optimizing mix design. This study introduces a computational framework using ensemble and non-ensemble machine learning (ML) techniques to model the yield stress (YS) and plastic viscosity (PV) of AAMs. A comprehensive dataset from previous experiments was used to train models, including artificial neural networks (ANN), gene expression programming (GEP), decision tree, random forest, adaptive boosting, and gradient boosting. The models achieved R² values exceeding 0.95 and correlation coefficients above 0.97. Error metrics, such as MAPE, MAE, MSE, and RMSE, further validated the models’ generalizability. A parametric analysis showed that a 20% increase in hydrated lime (HL) content raised YS by 350Pa, which is similar to the effect of varying fly ash (FA) from 60% to 100%. A 40% increase in FA and HL content increased PV by 4Pa.s and 25Pa.s, respectively. A 3% rise in sodium silicate led to a 400Pa increase in YS, while a similar increase in sodium hydroxide raised YS by 170Pa and PV by 15Pa·s. Overall, this study provides an effective tool for optimizing AAM mix designs by accurately predicting rheological properties and quantifying the impact of key parameters, contributing to the advancement of sustainable cementitious materials in construction.