Support vector machine (SVM) prediction of coefficients of curvature and uniformity of hybrid cement modified unsaturated soil with NQF inclusion
Loading...
Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
leaner Engineering and Technology
Abstract
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.
Description
Keywords
Support vector machine (SVM), Coefficient of curvature, Coefficient of uniformity, Model performance evaluation, Sensitivity analysis, Unsaturated soil, Waste base binders
Citation
Onyelowe, K. C., Mahesh, C. B., Srikanth, B., Nwa-David, C., Obimba-Wogu, J., & Shakeri, J. (2021). Support vector machine (SVM) prediction of coefficients of curvature and uniformity of hybrid cement modified unsaturated soil with NQF inclusion. Cleaner Engineering and Technology, 5, 100290. https://doi.org/10.1016/j.clet.2021.100290