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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Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
Nanotechnology for Environmental Engineering
Abstract
Sensitivity 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.
Description
Keywords
Sensitivity analysis, Erodibility, Erodible soil, Artificial neural network, Nanostructured quarry fines, Error analysis, Agiel neural network software
Citation
Onyelowe, 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/s41204-021-00131-2