Predicting Nanobinder-Improved Unsaturated Soil Consistency Limits Using Genetic Programming and Artificial Neural Networks
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Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
Applied Computational Intelligence and Soft Computing
Abstract
Unsaturated soils used as compacted subgrade, backfill, or foundation materials react unfavorably under hydraulically bound
environments due to swell and shrink cycles in response to seasonal changes. To overcome these undesirable conditions, additive
stabilization processes are used to improve the volume change phenomenon in soils. However, the use of supplementary binders
made from solid waste base powder materials has become necessary to deal with the hazards of greenhouse due to ordinary cement
use. Meanwhile, several studies are being carried out to design infrastructures even with the limitations of insufficient or lack of
equipment needed for efficient design performance. Intelligent prediction techniques have been used to overcome this shortcoming
as the primary purpose of this research work. +erefore, in this work, genetic programming (GP) and artificial neural
network (ANN) have been used to predict the consistency limits, i.e., liquid limits, plastic limit, and plasticity index of unsaturated
soil treated with a composite binder known as hybrid cement (HC) made from blending nanostructured quarry fines (NQF) and
hydrated-lime-activated nanostructured rice husk ash (HANRHA). +e database needed for the prediction operation was
generated from several experiments corresponding with treatment dosages of HANRHA between 0 and 12% at a rate of 0.1%. +e
results of the stabilization exercise showed substantial development on the soil properties examined, while the prediction exercise
showed that ANN outclassed GP in terms of performance evaluation, which was conducted using sum of squared error (SSE) and
coefficient of determination (R2) indices. Generally, nanostructuring of the component binder material has contributed to the
success achieved in both soil improvement and efficiency of the models predicted.
Description
Keywords
Soil Consistency Limits, Genetic Programming, Artificial Neural Networks
Citation
Ebid, A. M., Nwobia, L. I., Onyelowe, K. C., & Aneke, F. I. (2021). Predicting nanobinder-improved unsaturated soil consistency limits using genetic programming and artificial neural networks. Applied Computational Intelligence and Soft Computing, 2021. https://doi.org/10.1155/2021/5992628