Novel hybrid machine learning models including support vector machine with meta-heuristic algorithms in predicting unconfined compressive strength of organic soils stabilised with cement and lime

Each type of soil has different optimal soil stabilisation additive content. To design the optimal soil stabilisation component, reliable and efficient models are required. The study proposes the Machine Learning (ML) model Support Vector Regression (SVR) to predict the Unconfined Compressive Strength (UCS) of stabilised soil. To be able to deliver optimal performance, five metaheuristic algorithms: Simulated Annealing (SA), Random Restart Hill Climbing (RRHC), Particle swarm optimisation (PSO), Hunger Games Search (HGS) and Slime Mould Algorithm (SMA) are integrated with the SVR model. To explore the effect of the number of inputs on the model's performance, the data was divided into two scenarios of input variable number. ML models are evaluated by K-Fold and numerical indicators R2, RMSE and MAE. The results show that in Scenario 1, the SVR-HGS model has a higher predictive performance than other predictive models. While in Scenario 2, the SVR-PSO model gives better performance than the remaining predictive models. SHapley Additive exPlanation (SHAP) and Partial Dependence Plots 2D (PDP) were used to gain insight into the effects of variables on UCS, and the effects of cement and lime on the variables. Obtaining variables that have an important influence on the variation of stabilised soil UCS, in which cement is considered the most significant variable. The detection of A-line value is a relatively important predictor of UCS. At a suitable A-line value, it is possible to reduce the content of chemical stabilising agents (cement, lime) while maintaining the UCS value at a relative threshold.