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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

Author

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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
    Faculty of Computer Science &Engineering, Galala University, Suze 435611, Egypt
    Artificial Intelligence Research Center (AIRC), Ajman University, Ajman P.O. Box 346, United Arab Emirates
    School of Computer Science and Robotics, Tomsk Polytechnic University, Tomsk 634050, Russia)

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.

Suggested Citation

  • Abdelaziz El Shinawi & Rehab Ali Ibrahim & Laith Abualigah & Martina Zelenakova & 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, MDPI, vol. 9(24), pages 1-13, December.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:24:p:3295-:d:705449
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    References listed on IDEAS

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    1. George S. Atsalakis & Ioanna G. Atsalaki & Constantin Zopounidis, 2018. "Forecasting the success of a new tourism service by a neuro-fuzzy technique," Post-Print hal-02879866, HAL.
    2. Atsalakis, George S. & Atsalaki, Ioanna G. & Zopounidis, Constantin, 2018. "Forecasting the success of a new tourism service by a neuro-fuzzy technique," European Journal of Operational Research, Elsevier, vol. 268(2), pages 716-727.
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    Cited by:

    1. Laith Abualigah & Ali Diabat & Raed Abu Zitar, 2022. "Orthogonal Learning Rosenbrock’s Direct Rotation with the Gazelle Optimization Algorithm for Global Optimization," Mathematics, MDPI, vol. 10(23), pages 1-42, November.

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