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

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dc.contributor.authorOnyelowe, Kennedy C.
dc.contributor.authorMahesh, Chilakala B.
dc.contributor.authorSrikanth, Bandela
dc.contributor.authorNwa-David, Chidobere
dc.contributor.authorObimba-Wogu, Jesuborn
dc.contributor.authorShakeri, Jamshid
dc.date.accessioned2022-09-14T07:20:41Z
dc.date.available2022-09-14T07:20:41Z
dc.date.issued2021
dc.description.abstractSupport 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.en_US
dc.identifier.citationOnyelowe, 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.2021.100290en_US
dc.identifier.urihttps://doi.org/10.1016/j.clet.2021.100290
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/4716
dc.language.isoenen_US
dc.publisherleaner Engineering and Technologyen_US
dc.subjectSupport vector machine (SVM)en_US
dc.subjectCoefficient of curvatureen_US
dc.subjectCoefficient of uniformityen_US
dc.subjectModel performance evaluationen_US
dc.subjectSensitivity analysisen_US
dc.subjectUnsaturated soilen_US
dc.subjectWaste base bindersen_US
dc.titleSupport vector machine (SVM) prediction of coefficients of curvature and uniformity of hybrid cement modified unsaturated soil with NQF inclusionen_US
dc.typeArticleen_US
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