Novel computational methods to predict the compressive strength of hydrothermally solidified clay

Abstract

Hydrothermally solidified clay (HSC) is clay that has hardened through hydrothermal conditions, which involve high temperatures and pressures. The HSC has outstanding features that render it beneficial for various applications, including construction materials, ceramics, and various industrial uses. Furthermore, the production process of HSC, which can be used as an eco-friendly construction material, stands out because of its lower energy consumption, which is consistent with sustainable development objectives. Therefore, this study employed the applications of machine learning (ML) techniques, including stronger variable creator machines (SVCM), high-correlated variable creator machines (HCVCM), gene expression programming (GEP), multivariate adaptive regression splines (MARS), group method of data handling (GMDH), and combinations of GEP with SVCM and HCVCM, i.e., SVCM + GEP and HCVCM + GEP, to predict the compressive strength (CS), which is a crucial indicator of soil performance. Based on the proposed ML methods, mathematical equations were derived to predict CS using reliable, published experimental data sets. The performance of the predictive models for the prediction of CS was assessed using statistical measures, the objective function (OBJ) parameter, and the uncertainty method. In addition, the graphical plots, including scatter and Taylor diagrams, were evaluated to assess the effectiveness and accuracy of the suggested approaches. Overall, the results demonstrated that the SVCM method has the highest accuracy for predicting CS. Finally, a SHapley Additive exPlanations (SHAP) method, sensitivity analysis, and a parametric study were used to evaluate the performance of the best predictive model for predicting CS.

Graphical abstract

Keywords

hydrothermally solidified clay / machine learning methods / statistical parameters / sustainable development / eco-friendly construction material / compressive strength prediction

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