Predicting Nanobinder-Improved Unsaturated Soil Consistency Limits Using Genetic Programming and Artificial Neural Networks

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dc.contributor.authorEbid, Ahmed M.
dc.contributor.authorNwobia, Light I.
dc.contributor.authorOnyelowe, Kennedy C.
dc.contributor.authorAneke, Frank I.
dc.date.accessioned2022-09-12T13:19:57Z
dc.date.available2022-09-12T13:19:57Z
dc.date.issued2021
dc.description.abstractUnsaturated 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.en_US
dc.identifier.citationEbid, 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/10.1155/2021/5992628en_US
dc.identifier.urihttps://doi.org/10.1155/2021/5992628
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/4692
dc.language.isoenen_US
dc.publisherApplied Computational Intelligence and Soft Computingen_US
dc.subjectSoil Consistency Limitsen_US
dc.subjectGenetic Programmingen_US
dc.subjectArtificial Neural Networksen_US
dc.titlePredicting Nanobinder-Improved Unsaturated Soil Consistency Limits Using Genetic Programming and Artificial Neural Networksen_US
dc.typeArticleen_US
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