Geotechnical and microstructural analysis of high-volume fly ash stabilized clayey soil and machine learning application

Abstract

The weak soil stabilization using solid wastes is one of the most common solutions for improving geotechnical characteristics as well as for problematic waste dumping in landfills. The present experimental study aims to examine the effect of high-volume Class-F fly ash on the geotechnical and microstructural properties of clayey soil by adding them in ranges between 5 % and 50 %. The results show that as the amount of fly ash in clayey soil increases, properties like the specific gravity, plasticity index, permeability, optimum moisture content, maximum dry density and free swelling index improves. Moreover, these geotechnical properties were analyzed to develop machine learning models using three different algorithms, namely K-nearest neighbor regression, random forest, and support vector regression, for obtaining the optimum amount of fly ash contents in weak expansive soils. The predicted and experimental results found to be in close-relation for predicting the geotechnical behavior of modified clayey soil. Furthermore, the performance of the ML models degrades as the number of components reduces, with KNN regression consistently outperforming SVR and RF but suffering significantly with fewer components. The results of the testing set in the case of four components are MSE of 77, R² of 0.896, RMSE of 0.846, MAE of 0.327, and SEE of 0.858, indicating precise and consistent predictions. However, the prediction accuracy considering lesser components shows MSE as 262, R² as 0.648, MAE as 5.606, SEE as 16.707, and GPI as 1.056, confirming the elevated error rates. Overall, it has been concluded that combining comprehensive experimental work and machine learning techniques outperforms in enhancing geotechnical data processing, optimized waste contents in weak soils, improves sustainability in construction, saves resources, reduces the possibility of human mistakes, and increases reliability.

Keywords

• Clay stabilization
• Waste reduction
• Machine learning (ML) modeling
• K-nearest neighbors (KNN)
• Design-mix optimization
• Support vector regression (SVR)