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Implementing an ANN model optimized by genetic algorithm for estimating cohesion of limestone samples

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Abstract

Shear strength parameters such as cohesion are the most significant rock parameters which can be utilized for initial design of some geotechnical engineering applications. In this study, evaluation and prediction of rock material cohesion is presented using different approaches i.e., simple and multiple regression, artificial neural network (ANN) and genetic algorithm (GA)-ANN. For this purpose, a database including three model inputs i.e., p-wave velocity, uniaxial compressive strength and Brazilian tensile strength and one output which is cohesion of limestone samples was prepared. A meaningful relationship was found for all of the model inputs with suitable performance capacity for prediction of rock cohesion. Additionally, a high level of accuracy (coefficient of determination, R 2 of 0.925) was observed developing multiple regression equation. To obtain higher performance capacity, a series of ANN and GA-ANN models were built. As a result, hybrid GA-ANN network provides higher performance for prediction of rock cohesion compared to ANN technique. GA-ANN model results (R 2 = 0.976 and 0.967 for train and test) were better compared to ANN model results (R 2 = 0.949 and 0.948 for train and test). Therefore, this technique is introduced as a new one in estimating cohesion of limestone samples.

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Authors and Affiliations

  1. Faculty of Science and Technology, Federation University Australia, PO Box 663, Ballarat, VIC, 3353, Australia

    Manoj Khandelwal

  2. Environmental Engineering and Green Technology Department, Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia

    Aminaton Marto & Danial Jahed Armaghani

  3. Department of Civil and Environmental Engineering, Amirkabir University of Technology, 15914, Tehran, Iran

    Seyed Alireza Fatemi & Danial Jahed Armaghani

  4. Young Researchers and Elites Club, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Mahyar Ghoroqi

  5. School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran

    Mahyar Ghoroqi

  6. Department of Earth Sciences, Indian Institute of Technology Bombay, Powai, Mumbai, 400 076, India

    T. N. Singh

  7. Young Researchers and Elite Club, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Omid Tabrizi

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  1. Manoj Khandelwal
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  2. Aminaton Marto
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  3. Seyed Alireza Fatemi
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  4. Mahyar Ghoroqi
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  5. Danial Jahed Armaghani
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  6. T. N. Singh
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  7. Omid Tabrizi
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Correspondence to Manoj Khandelwal.

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Khandelwal, M., Marto, A., Fatemi, S.A. et al. Implementing an ANN model optimized by genetic algorithm for estimating cohesion of limestone samples. Engineering with Computers 34, 307–317 (2018). https://doi.org/10.1007/s00366-017-0541-y

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  • DOI: https://doi.org/10.1007/s00366-017-0541-y

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