Intelligent prediction of coefficients of curvature and uniformity of hybrid cement modified unsaturated soil with NQF inclusion
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Date
2021
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Cleaner Engineering and Technology
Abstract
The cost and sophisticated equipment required to conduct earthwork laboratory experiments have been of
concern to the design and performance monitoring of infrastructures in recent times. Lateritic soils especially
those under unsaturated conditions are erratic and deserve close attention in terms of laboratory studies. In order
to overcome the rigors and time consumed during experimental procedures, soft computing has been used to
predict soil parameters for the purpose of design and construction. In this work, the ANN, GEP and LMR were
employed to predict the coefficients of curvature and uniformity of lateritic soil treated with multiple binders
locally generated, which were hybrid cement (HC) and nanostructured quarry fines (NQF). The effect of the
varying dosages of HC and NQF added to the soil were studied and the behavior of clay activity, clay content,
frictional angle, coefficients of curvature and uniformity were measure. 121 datasets were generated from the
experimental exercise for the selected parameter both for the predictors and for the targets. These datasets were
deployed in the ratio of 70 is to 30% for training and testing of the models predictions respectively. The performances
of the models were evaluated using error analysis (VAF, RMSE, MAE) and accuracy (R2) indices and it
was observed that the ANN outclassed both GEP and LMR due to its speed and robustness in adopting backpropagation
and feed-forward algorithms. Furthermore, the sensitivity analysis showed that F, C, H (HC),
NQF and Ac in that order of most influential to least influential influenced the behavior of the Cc model with H
(HC) and NQF showing equal effect on the Cc. Also, H (HC), NQF, F, C and Ac in that order of influence from
most to least affected the behavior of the Cu predicted model also with HC and NQF having equal effect on the
Cu. Generally, the learning techniques showed good performance in predicting the outputs hence are good
techniques to be utilized in design and performance evaluation.
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Keywords
Intelligent prediction, Coefficient of curvature (CC), Coefficient of uniformityCU, Hybrid cement (HC), Artificial neural network (ANN)
Citation
Onyelowe, K. C., & Shakeri, J. (2021). Intelligent prediction of coefficients of curvature and uniformity of hybrid cement modified unsaturated soil with NQF inclusion. Cleaner Engineering and Technology, 4, 100152. https://doi.org/10.1016/j.clet.2021.100152