Hybrid XGB model for predicting unconfined compressive strength of solid waste-cement-stabilized cohesive soil

Abstract

The utilization of cement has been found to have negative environmental impacts. In order to reduce the quantity of cement used and improve the mechanical properties of solid waste-cement-stabilized cohesive soil, the incorporation of solid waste as additives has been investigated. Unconfined compressive strength is a crucial parameter in geotechnical engineering. However, existing empirical formulas have limited accuracy and applicability when it comes to the unconfined compressive strength of solid waste-cement-stabilized cohesive soil. The machine learning model can be used to provide accurate and comprehensive predictions by considering the nonlinear relationships between independent and dependent variables. This study aims to propose a machine learning model tuned by optimization algorithms with high generalization performance in accurately predicting the unconfined compressive strength. Firstly, a database containing 474 specimens was developed. Secondly, eight machine learning models were established, composed five single models and three hybrid models, to train and test the database. Six performance indicators were employed to evaluate the generalization ability of these models. Finally, the optimal model was selected for analysis of the importance of the feature variables using shapley additive explanations, which were compared with those of the existing empirical model. The research findings indicated that, the extreme gradient boosting model tuned with tree-structured parzen estimators exhibited the highest predictive accuracy and generalization ability. The curing age, cement content, plastic limit, and water content were identified as the most critical factors influencing the unconfined compressive strength. Among the chemical components in solid waste, the aluminum oxide content and silicon dioxide content were found to significantly influence the unconfined compressive strength, while the impact of calcium oxide content was relatively minor. Furthermore, the optimal solid waste content was found to be around 10 %. This study made a significant contribution to the effective utilization of waste resources in the context of sustainable construction practices.