Support vector machine (SVM) prediction of coefficients of curvature and uniformity of hybrid cement modified unsaturated soil with NQF inclusion

• Main Authors: Kennedy C. Onyelowe, Chilakala B. Mahesh, Bandela Srikanth, Chidobere Nwa-David, Jesuborn Obimba-Wogu, Jamshid Shakeri
• Format: Article
• Language: English
• Published: Elsevier 2021-12-01
• Series: Cleaner Engineering and Technology
• Subjects: Support vector machine (SVM); Coefficient of curvature; Coefficient of uniformity; Model performance evaluation; Sensitivity analysis; Unsaturated soil
• Online Access: http://www.sciencedirect.com/science/article/pii/S2666790821002500

Support vector machine (SVM) with its feature known as the statistical risk minimization (SRM) has been employed in the prediction of coefficient of curvature and uniformity on unsaturated lateritic soil treated with composites of hybrid cement and nanostructured quarry fines. This feature utilized by SVM is the advantage it exercises over other intelligent learning techniques. This prediction has become necessary due to the time and equipment needs required to regularly conduct laboratory experiments prior to earthwork designs and construction. It is important to note that earthwork projects involving unsaturated soils pose threats of failure due to volume changes during seasonal cycles of wetting and drying especially for hydraulically bound environments and substructures. With an intelligent prediction, these design and construction worries are overcome. The soil used in the current work has been classified as an A-7-6 group soil with highly plastic consistency. Multiple experiments were conducted to generate multitude of datasets for the hybrid cement, nanostructured quarry fines, clay content and activity and frictional angle, which were selected as the independent variables for the model to predict coefficients of curvature and uniformity as the dependent variables. In order to correlate the relationship between the input and output parameters and as well validate the SVM model, detailed statistical analysis including Pearson's coefficient of correlation (R) and determination (R2) and error analysis were conducted. Based upon the statistical analysis, the parameters were observed to have good correlation and determination ranging between 0.97 and 0.99. It was also observed that SVM outclassed MLR more in predicting Cu then it did in predicting Cc. Finally, sensitivity analysis was carried out and it was found that the Cc value is dependent mostly on frictional angle while Cu is dependent most on the NQF.