Estimation of the erodibility of treated unsaturated lateritic soil using support vector machine-polynomial and -radial basis function and random forest regression techniques

Support vector machine techniques (polynomial and radial basis function) and random forest regression techniques have been used to predict erodibility of an unsaturated soil collected from an erosion watershed located at Amuzukwu, Nigeria. This location has been on the spotlight due to the level des...

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Bibliographic Details
Published in:	Cleaner Materials 2022-03, Vol.3, p.100039, Article 100039
Main Authors:	Onyelowe, Kennedy C., Gnananandarao, Tammineni, Ebid, Ahmed M.
Format:	Article
Language:	eng
Subjects:	Cleaner/greener binders Erodibility Nanotextured Composite Binder (NCB) Radial basis function (RBF) Random forest regression Support vector machine-polynomial (SVM-p) Unsaturated soil
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Summary:	Support vector machine techniques (polynomial and radial basis function) and random forest regression techniques have been used to predict erodibility of an unsaturated soil collected from an erosion watershed located at Amuzukwu, Nigeria. This location has been on the spotlight due to the level destruction resulting from erosion gullies. The soil, which was found to be of “Benin formation”, has been treated with composites of waste-based binders in a cleaner and greener technology to avoid the conventional emissions emanating from the use of ordinary cement. These materials were applied in a nanotextured scale, which improved the reactive surface and nucleating ability of the supplementary cementitious additives. This work is focused on proposing more reliable intelligent models, which can be used in future designs, construction and monitoring of the performance of the facilities at the erosion site without relying on repeated laboratory experiments. The performance indices, which included r, R2, MSE, RMSE, MAE and MAPE were used to test the validity and accuracy of the models. The three models showed agreement with the performance accuracy though SVM-radial basis function (SVM-RBF) outclassed both the previously used ANN and the SVM-p and RFR with R2 of 0.999 and minimal errors. The additives also proved to be good greener and cleaner cementing additives in the stabilization of soils with substantial improvements.
ISSN:	2772-3976 2772-3976