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Article

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

by
Abdelaziz El Shinawi
1,
Rehab Ali Ibrahim
2,
Laith Abualigah
3,
Martina Zelenakova
4,* and
Mohamed Abd Elaziz
2,5,6,7
1
Environmental Geophysics Lab (ZEGL), Geology Department, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
2
Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
3
Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan
4
Department of Environmental Engineering, Faculty of Civil Engineering, Technical University of Kosice, 04200 Kosice, Slovakia
5
Faculty of Computer Science &Engineering, Galala University, Suze 435611, Egypt
6
Artificial Intelligence Research Center (AIRC), Ajman University, Ajman P.O. Box 346, United Arab Emirates
7
School of Computer Science and Robotics, Tomsk Polytechnic University, Tomsk 634050, Russia
*
Author to whom correspondence should be addressed.
Mathematics 2021, 9(24), 3295; https://doi.org/10.3390/math9243295
Submission received: 21 November 2021 / Revised: 11 December 2021 / Accepted: 14 December 2021 / Published: 18 December 2021
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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 machine learning techniques; liquid limit; clay fraction; swelling potentiality

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MDPI and ACS Style

El Shinawi, A.; Ibrahim, R.A.; Abualigah, L.; Zelenakova, M.; Abd Elaziz, M. 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. Mathematics 2021, 9, 3295. https://doi.org/10.3390/math9243295

AMA Style

El Shinawi A, Ibrahim RA, Abualigah L, Zelenakova M, Abd Elaziz M. 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. Mathematics. 2021; 9(24):3295. https://doi.org/10.3390/math9243295

Chicago/Turabian Style

El Shinawi, Abdelaziz, Rehab Ali Ibrahim, Laith Abualigah, Martina Zelenakova, and Mohamed Abd Elaziz. 2021. "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" Mathematics 9, no. 24: 3295. https://doi.org/10.3390/math9243295

APA Style

El Shinawi, A., Ibrahim, R. A., Abualigah, L., Zelenakova, M., & Abd Elaziz, M. (2021). 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. Mathematics, 9(24), 3295. https://doi.org/10.3390/math9243295

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