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

Cleaner Engineering and Technology

Bandela Srikanth¹  Chilakala B. Mahesh²  Kennedy C. Onyelowe³  Jamshid Shakeri⁴  Jesuborn Obimba-Wogu⁵  Chidobere Nwa-David⁶ 

[1] Corresponding author. .;Department of Civil and Mechanical Engineering, Kampala International University, Kampala, Uganda;;Department of Civil Engineering, Michael Okpara University of Agriculture, Umudike Nigeria &Department of Civil Engineering, Michael Okpara University of Agriculture, Umudike, Nigeria;Department of Civil Engineering, Vishnu Institute of Technology, Bhimavaram, Andhrapradesh, India;Department of Computer Science and Engineering, Vishnu Institute of Technology, Bhimavaram, India;

关键词: Support vector machine (SVM);    Coefficient of curvature;    Coefficient of uniformity;    Model performance evaluation;    Sensitivity analysis;    Unsaturated soil;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown