Machine learning approach for predicting and evaluating California bearing ratio of stabilized soil containing industrial waste

Abstract

The California bearing ratio (CBR) is one of the important indexes, which is used to represent the strength of subgrade or subbases of pavement. In general, the CBR can be determined through experiments both in the laboratory and field. However, the determination of the CBR is time and cost-consuming as well as low accuracy due to the disturbance of samples and limitations of preparation in the laboratory. Thus, this study estimates the CBR of stabilized soil using twelve machine learning techniques (6 single models and 6 hybrid models). The single models include artificial neural network (ANN), gradient boosting (GB), extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN), while the six hybrid models are a combination of these single models and random restart hill-climbing optimization (RRHC). Twelve models are constructed based on eleven input variables, including cement, Atterberg's limits, optimum moisture content (OMC), maximum dry density (MDD), and dust and ashes. To evaluate the performance of the proposed models, four popular statistical indexes namely mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and determination coefficient (R2) were used. The results indicate that among twelve models, four models using GB, RRHC_XGB, RRHC_RF, and RF had a high prediction accuracy (R2 > 0.98) and outperformed than other remaining models. Among these four models, the model using RF has the highest prediction accuracy (R2 = 0.9817, RMSE = 2.3970, MAE = 1.1682, MAPE = 0.0666). According to the result of feature importance analysis using Sklearn permutation importance, SHapley Additive exPlanation (SHAP), individual conditional expectation (ICE), and partial dependence plots-2D, cement and plasticity index (PI) are two most important variables affecting the CBR prediction of stabilized soil. Where, PI was found to be a very crucial factor, which improved the prediction ability of the models compared to the results of previous studies. The results also reveal that when cement content is larger than 2%, there is an insignificant influence of the cement on the CBR of stabilized soil. The value of PI smaller than 15% has a vital impact on the CBR for any values of cement, dust, and ashes. Furthermore, the results also indicate that dust and ash content have less effect on the CBR of stabilized soil. In summary, it can be concluded that this study provides an insightful assessment of the CBR prediction of stabilized soil, and the results of this study can fill the gap in the literature and provide practical knowledge and application on the CBR of stabilized soil.