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Evolving support vector regression using Grey Wolf optimization; forecasting the geomechanical properties of rock

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Abstract

The geomechanical properties of rock, including shear strength (SS) and uniaxial compressive strength (UCS), are very important parameters in designing rock structures. To improve the accuracy of SS and UCS prediction, this study presented an evolving support vector regression (SVR) using Grey Wolf optimization (GWO). To examine the feasibility and applicability of the SVR-GWO model, the differential evolution (DE) and artificial bee colony (ABC) algorithms were also used. In other words, the SVR hyperparameters were tuned using the GWO, DE, and ABC algorithms. To implement the proposed models, a comprehensive database accessible in an open-source was used in this study. Finally, the comparative experiments such as root mean square error (RMSE) were conducted to show the superiority of the proposed models. The SVR-GWO model predicted the SS and UCS with RMSE of 0.460 and 3.208, respectively, while, the SVR-DE model predicted the SS and UCS with RMSE of 0.542 and 5.4, respectively. Furthermore, the SVR-ABC model predicted the SS and UCS with RMSE of 0.855 and 5.033, respectively. The aforementioned results clearly exhibited the applicability as well as the usability of the proposed SVR-GWO model in the prediction of both SS and UCS parameters. Accordingly, the SVR-GWO model can be also applied to solving various complex systems, especially in geotechnical and mining fields.

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Authors and Affiliations

  1. State Key Laboratory of Safety and Health for Metal Mines, Sinosteel Maanshan Institute of Mining Research, Co., Ltd., Maanshan, 243000, Anhui, China

    Chuanhua Xu

  2. Département Etudes Thermodynamiques, Division Laboratoires, Sonatrach, Boumerdes, Algeria

    Menad Nait Amar

  3. Institut des Sciences Analytiques et de Physico-Chimie Pour l’Environnement et les Matériaux, IPREM, UMR 5254, CNRS Université de Pau et des Pays de l’Adour/E2S, 2 Avenue P. Angot, Technopôle Hélioparc, 64000, Pau, France

    Mohammed Abdelfetah Ghriga

  4. Laboratoire Génie Physique des Hydrocarbures, Faculté des Hydrocarbures et de la Chimie, Université M’Hamed Bougara de Boumerdes, Avenue de l’Indépendance, 35000, Boumerdes, Algeria

    Mohammed Abdelfetah Ghriga

  5. Département Gisement Miniers et Pétroliers, Université M’Hamed Bougara de Boumerdes, Boumerdes, Algeria

    Hocine Ouaer

  6. State Key Laboratory of Safety and Health for Metal Mines, Sinosteel Maanshan Institute of Mining Research, Co., Ltd., Maanshan, 243000, Anhui, China

    Xiliang Zhang

  7. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Mahdi Hasanipanah

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  1. Chuanhua Xu
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  2. Menad Nait Amar
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  3. Mohammed Abdelfetah Ghriga
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  4. Hocine Ouaer
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  5. Xiliang Zhang
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  6. Mahdi Hasanipanah
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Xu, C., Nait Amar, M., Ghriga, M.A. et al. Evolving support vector regression using Grey Wolf optimization; forecasting the geomechanical properties of rock. Engineering with Computers 38, 1819–1833 (2022). https://doi.org/10.1007/s00366-020-01131-7

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