Erodibility of Nanocomposite-Improved Unsaturated Soil Using Genetic Programming, Artificial Neural Networks, and Evolutionary Polynomial Regression Techniques

Kennedy C. Onyelowe, Ahmed M. Ebid, Uchenna Egwu, Michael E. Onyia, Hyginus N. Onah, Light I. Nwobia, Izuchukwu Onwughara and Ali Akbar Firoozi
Additional contact information
Kennedy C. Onyelowe: Department of Civil and Mechanical Engineering, Kampala International University, Kampala P.O. Box 20000, Uganda
Ahmed M. Ebid: Department of Structural Engineering, Faculty of Engineering and Technology, Future University, New Cairo 11845, Egypt
Uchenna Egwu: Department of Civil Engineering, School of Civil Engineering and Geosciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Michael E. Onyia: Department of Civil Engineering, Faculty of Engineering, University of Nigeria, Nsukka 410001, Nigeria
Hyginus N. Onah: Department of Civil Engineering, Faculty of Engineering, University of Nigeria, Nsukka 410001, Nigeria
Light I. Nwobia: Department of Civil Engineering, Michael Okpara University of Agriculture, Umudike 440101, Nigeria
Izuchukwu Onwughara: Nigeria Erosion and Watershed Management Project, Abia State Ministry of Environment and Ministry of Works, Umuahia 440001, Nigeria
Ali Akbar Firoozi: Department of Civil Engineering, University of Botswana, Gaborone 0061, Botswana

Sustainability, 2022, vol. 14, issue 12, 1-19

Abstract: Genetic programming (GP) of four levels of complexity, including artificial neural networks of the hyper-tanh activation function (ANN-Hyper-Tanh), artificial neural networks of the sigmoid activation function (ANN-Sigmoid), evolutionary polynomial regression (optimized with genetic algorithm) (EPR), and intelligent techniques have been used to predict the erodibility of lateritic soil collected from an erosion site and treated with hybrid cement. Southeastern Nigeria and specifically Abia State is being destroyed by gully erosion, the solution of which demands continuous laboratory examinations to determine the parameters needed to design sustainable solutions. Furthermore, complicated equipment setups are required to achieve reliable results. To overcome constant laboratory works and equipment needs, intelligent prediction becomes necessary. This present research work adopted four different metaheuristic techniques to predict the erodibility of the soil; classified as A-7-6, weak, unsaturated, highly plastic, high swelling and high clay content treated with HC utilized in the proportions of 0.1–12% at the rate of 0.1%. The results of the geotechnics aspect of the work shows that the HC, which is a cementitious composite formulated from blending nanotextured quarry fines (NQF) and hydrated lime activated nanotextured rice husk ash (HANRHA), improves the erodibility of the treated soil substantially and consistently. The outcome of the prediction models shows that EPR with SSE of 1.6% and R 2 of 0.996 outclassed the other techniques, though all four techniques showed their robustness and ability to predict the target (Er) with high performance accuracy.

Keywords: unsaturated soil; ANN-HyperTanh; ANN-Sigmoid; evolutionary polynomial regression (EPR); HC-treated soil; erodibility of pavement materials (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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