Sensitivity analysis and prediction of erodibility of treated unsaturated soil modified with nanostructured fines of quarry dust using novel artificial neural network

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dc.contributor.authorOnyelowe, Kennedy C.
dc.contributor.authorGnananandara, Tammineni
dc.contributor.authorNwa‑David, Chidobere
dc.date.accessioned2022-09-13T20:31:09Z
dc.date.available2022-09-13T20:31:09Z
dc.date.issued2021
dc.description.abstractSensitivity and error analyses and machine-based prediction have been conducted on the erodibility response of erodible unsaturated soil (degree of saturation 60%) treated with local cement and modified with nanostructured quarry fines. The machine-based exercise has become necessary because of the incessant washing away of soil on erosion watersheds causing devastating gullies around the developing world and the need to propose model equations to study, design and proffer future solutions to this environmental problem. Also, in order to overcome complex experimental setup needed to repeatedly study erosion problems, there is also need to forecast model equations by employing variables that can easily be determined as predictors of the model. This work was aimed at the prediction of erodibility and generating a model equation using the ANN learning technique. The erodible soil was collected and classified as poorly graded, highly plastic and as an A–7–6 group. 121 datasets were generated from multiple experiments for the input parameters and deployed in model training and testing in the ratio of 70 to 30%, respectively. The model performance was validated and error analysis was conducted using R2, MAE, MSE, RMSE and MAPE indices. The performance showed that the model has R2 of more than 0.95 in both training and testing between the predicted and measured values. Also, the error indices showed significantly small values, which showed good performance. Finally, the sensitivity analysis outcome showed that the liquid limit was the most influential on the erodibility model results. Generally, ANN technique has shown to be very flexible in forecasting civil engineering problems and fundamentally in proposing model equations.en_US
dc.identifier.citationOnyelowe, K. C., Gnananandarao, T., & Nwa-David, C. (2021). Sensitivity analysis and prediction of erodibility of treated unsaturated soil modified with nanostructured fines of quarry dust using novel artificial neural network. Nanotechnology for Environmental Engineering, 6(2), 1-11. https://doi.org/10.1007/s41204-021-00131-2en_US
dc.identifier.urihttps://doi.org/10.1007/s41204-021-00131-2
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/4711
dc.language.isoenen_US
dc.publisherNanotechnology for Environmental Engineeringen_US
dc.subjectSensitivity analysisen_US
dc.subjectErodibilityen_US
dc.subjectErodible soilen_US
dc.subjectArtificial neural networken_US
dc.subjectNanostructured quarry finesen_US
dc.subjectError analysisen_US
dc.subjectAgiel neural network softwareen_US
dc.titleSensitivity analysis and prediction of erodibility of treated unsaturated soil modified with nanostructured fines of quarry dust using novel artificial neural networken_US
dc.typeArticleen_US
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