Prediction of Compressive Strength in Fine-Grained Soils Stabilized with a Combination of Various Stabilization Agents and Nano-SiO2 Using Machine Learning Algorithms

Conducting laboratory tests in geotechnical engineering is a costly, time-consuming, and labor-intensive process. As an alternative solution, this study employs various machine learning methods to predict the unconfined compressive strength (UCS) of fine-grained soils stabilized by combining chemical additives (such as Portland cement, lime, and industrial and agricultural waste) and nanosilica. After preparing a comprehensive database of a collection of studies from the literature, ten machine learning models were developed for modeling, and their performances were compared using various metrics. After comparing the performance of the models in predicting the UCS with experimental results, the CatBoost model was determined as the optimal model. The variables of curing time, liquid limit of soil, and additive contents were identified as the most effective parameters on the stabilized soil’s UCS. The best-performing model on the applied dataset was determined and compared with experimental models. After determining the effective parameters for predicting the strength of stabilized soil, the nonlinear relationships between the most important variables and the stabilized soil’s UCS were analyzed and investigated.