Geopolymer-stabilized soils: influencing factors, strength development mechanism and sustainability

Abstract

This paper presents the strength and micro-structural characteristics of high plasticity expansive clay stabilized with ordinary Portland cement (OPC) and geopolymers. Furthermore, sustainability aspects such as cost-efficiency, energy consumption and eco-efficiency of the OPC and geopolymer-stabilized soils were compared. The experimental results revealed that the geopolymer-stabilized soils exhibit higher strength and sustainability performance than the OPC-stabilized soils. This study also explores the developed machine learning prediction models of the geopolymer soils’ unconfined compressive strength (UCS) based on experimental data from current research and previous literature. Linear regression (LR), K-nearest neighbour (KNN), random forest (RF), random forest with random search hyperparameter optimization (BRRF) and random forest with grid search hyperparameter optimization (BGRF) were used as part of ensemble algorithms to predict stabilized soils UCS. Eight parameters such as liquid limit (LL), plasticity index (PI), ground granulated blast furnace slag (S) content, fly ash (FA) content, the molarity of NaOH (M), activator to binder ratio (A/B), Na/Al and Si/Al were used as input to predict UCS of geopolymer soils. The following metrics were used to assess the models’ predictive ability for compressive strength: coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and mean square error (MSE). The BRRF model has a good potential to predict the UCS of geopolymer soils, according to the findings of its testing (MAE = 0.27, MSE = 0.21, RMSE = 0.46, R2 = 0.99) and training (MAE = 0.78, MSE = 1.48, RMSE = 1.23, R2 = 0.96) phases. According to the RF model's feature importance study, slag content and liquid limit were found to influence forecasting compressive strength, while fly ash content has the least influence.

Keywords

• Geopolymerization
• precursors
• Na/Al
• random forest
• sustainability