Enhanced Adaptive Neuro-Fuzzy Inference System Using Reptile Search Algorithm for Relating Swelling Potentiality Using Index Geotechnical Properties: A Case Study at El Sherouk City, Egypt

Abdelaziz El Shinawi, Rehab Ali Ibrahim, Laith Abualigah, Martina Zelenakova and Mohamed Abd Elaziz
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Abdelaziz El Shinawi: Environmental Geophysics Lab (ZEGL), Geology Department, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
Rehab Ali Ibrahim: Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
Laith Abualigah: Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan
Martina Zelenakova: Department of Environmental Engineering, Faculty of Civil Engineering, Technical University of Kosice, 04200 Kosice, Slovakia
Mohamed Abd Elaziz: Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt

Mathematics, 2021, vol. 9, issue 24, 1-13

Abstract: The swelling potentiality is a vital property of fine-grained soils strictly related to the index properties and chemical composition. The integration of machine learning techniques and geotechnical parameters provided a new integrative approach for predicting the free swelling index (FSI) and the swelling pressure (SP). In this paper, an adaptive neuro-fuzzy inference system (ANFIS) using named Reptile Search Algorithm (RSA) is presented to predict the swelling potentiality for fine-grained soils in the foundation bed at El Sherouk city, Egypt. The developed predictive model, named RSA-ANFIS, used as input measured 108 natural fine-grained soil samples of index geotechnical parameters and chemical composition as input data and the measured data of the free swelling index and the swelling pressure as output data. To justify the performance of the developed model, a comparative study was carried out, and the results show that the developed RSA-ANFIS has a high performance over the competitive methods in terms of coefficient of determination, root mean square error (RMSE), and mean absolute error (MAE). This new integrative approach is considered at the highly developed stage to predict and improve the analysis of multi-parameter soil behavior and could be applied in other objective variable datasets.

Keywords: machine learning techniques; liquid limit; clay fraction; swelling potentiality (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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