Geomechanics and Engineering

  • 제36권5호
  • /
  • Pages.465-474
  • /
  • 2024
  • /
  • 2005-307X(pISSN)
  • /
  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

The gene expression programming method for estimating compressive strength of rocks

  • Ibrahim Albaijan (Mechanical Engineering Department, College of Engineering at Al-Kharj, Prince Sattam Bin Abdulaziz University) ;
  • Daria K. Voronkova (Department of Mathematics and Natural Sciences, Gulf University for Science and Technology, Mishref Campus) ;
  • Laith R. Flaih (Department of Computer Science, Cihan University-Erbil) ;
  • Meshel Q. Alkahtani (Civil Engineering Department, College of Engineering, King Khalid University) ;
  • Arsalan Mahmoodzadeh (IRO, Civil Engineering Department, University of Halabja) ;
  • Hawkar Hashim Ibrahim (Department of Civil Engineering, College of Engineering, Salahaddin University-Erbil) ;
  • Adil Hussein Mohammed (Department of Communication and Computer Engineering, Faculty of Engineering, Cihan University-Erbil)
  • 투고 : 2023.04.18
  • 심사 : 2024.02.15
  • 발행 : 2024.03.10
⟨ 이전 논문 다음 논문 ⟩

초록

Uniaxial compressive strength (UCS) is a critical geomechanical parameter that plays a significant role in the evaluation of rocks. The practice of indirectly estimating said characteristics is widespread due to the challenges associated with obtaining high-quality core samples. The primary aim of this study is to investigate the feasibility of utilizing the gene expression programming (GEP) technique for the purpose of forecasting the UCS for various rock categories, including Schist, Granite, Claystone, Travertine, Sandstone, Slate, Limestone, Marl, and Dolomite, which were sourced from a wide range of quarry sites. The present study utilized a total of 170 datasets, comprising Schmidt hammer (SH), porosity (n), point load index (Is(50)), and P-wave velocity (Vp), as the effective parameters in the model to determine their impact on the UCS. The UCS parameter was computed through the utilization of the GEP model, resulting in the generation of an equation. Subsequently, the efficacy of the GEP model and the resultant equation were assessed using various statistical evaluation metrics to determine their predictive capabilities. The outcomes indicate the prospective capacity of the GEP model and the resultant equation in forecasting the unconfined compressive strength (UCS). The significance of this study lies in its ability to enable geotechnical engineers to make estimations of the UCS of rocks, without the requirement of conducting expensive and time-consuming experimental tests. In particular, a user-friendly program was developed based on the GEP model to enable rapid and very accurate calculation of rock's UCS, doing away with the necessity for costly and time-consuming laboratory experiments.

키워드

과제정보

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through large group Research Project under grant number RGP2/68/44.

참고문헌

  1. Asheghi, R., Abbaszadeh Shahri, A. and Khorsand Zak, M. (2019), "Prediction of uniaxial compressive strength of different quarried rocks using Metaheuristic Algorithm", Arab. J. Sci. Eng., 44, 8645-8659. https://doi.org/10.1007/s13369-019-04046-8.
  2. Alvarez Grima, M. and Babuska, R. (1999), "Fuzzy model for the prediction of unconfined compressive strength of rock samples", Int. J. Rock. Mech. Min. Sci., 36, 339-349. https://doi.org/10.1016/S0148-9062(99)00007-8.
  3. Ferreira, C. (2006), "Gene expression programming", Springer Berlin Heidelberg, 21. https://doi.org/10.1007/3-540-32849-1.
  4. Gultekin, N.Y., Gokceoglu, C. and Sezer, E.A. (2013), "Prediction of uniaxial compressive strength of granitic rocks by various non-linear tools and comparison of their performances", Int. J. Rock. Mech. Min. Sci., 62, 113-122. https://doi.org/10.1016/j.ijrmms.2013.05.005.
  5. Hosseini, S. and Al Khaled, A. (2014), "A survey on the imperialist competitive algorithm metaheuristic: implementation in engineering domain and directions for future research", Appl. Soft Comput. J., 24, 1078-1094. https://doi.org/10.1016/j.asoc.2014.08.024.
  6. Huang, H., Yuan, Y., Zhang, W. and Zhu, L. (2021), "Property assessment of high-performance concrete containing three types of fibers", Int. J. Concrete Struct. Mater., 15(1), 39. https://doi.org/10.1186/s40069-021-00476-7.
  7. Jia, S., Dai, Z., Zhou, Z., Ling, H., Yang, Z., Qi, L., Wang, Z., Zhang, X., Thanh, H.V. and Soltanian, M.R. (2023), "Upscaling dispersivity for conservative solute transport in naturally fractured media", Water Res., 235, 119844. https://doi.org/10.1016/j.watres.2023.119844.
  8. Kidega, R., Ondiaka, M.N., Maina, D., Jonah, K.A.T. and Kamran, M. (2022), "Decision based uncertainty model to predict rockburst in underground engineering structures using gradient boosting algorithms", Geomech. Eng., 30(3), 259-272. https://doi.org/10.12989/gae.2022.30.3.259.
  9. Long, X., Mao, M., Su, T., Su, Y. and Tian, M. (2023), "Machine learning method to predict dynamic compressive response of concrete-like material at high strain rates", Defence Technol., 23, 100-111. https://doi.org/10.1016/j.dt.2022.02.003.
  10. Li, J., Liu, Y. and Lin, G. (2023), "Implementation of a coupled FEM-SBFEM for soil-structure interaction analysis of largescale 3D base-isolated nuclear structures", Comput. Geotech., 162, 105669. https://doi.org/10.1016/j.compgeo.2023.105669.
  11. Liu, C., Cui, J., Zhang, Z., Liu, H., Huang, X. and Zhang, C. (2021a), "The role of TBM asymmetric tail-grouting on surface settlement in coarse-grained soils of urban area: Field tests and FEA modelling", Tunn. Undergr. Sp. Tech., 111, 103857. https://doi.org/10.1016/j.tust.2021.103857.
  12. Liu, W., Zhou, H., Zhang, S. and Zhao, C. (2023), "Variable parameter creep model based on the separation of viscoelastic and viscoplastic deformations", Rock Mech. Rock Eng., 56(6), 4629-4645. https://doi.org/10.1007/s00603-023-03266-7.
  13. Liu, J., Jiang, Y., Zhang, Y. and Sakaguchi, O. (2021b), "Influence of different combinations of measurement while drilling parameters by artificial neural network on estimation of tunnel support patterns", Geomech. Eng., 25(6), 439-454. https://doi.org/10.12989/gae.2021.25.6.439.
  14. Mahdiyar, A., Jahed Armaghani, D., Marto, A., Nilashi, M. and Ismail, S. (2019), "Rock tensile strength prediction using empirical and soft computing approaches", Bull. Eng. Geol. Environ., 78, 4519-4531. https://doi.org/10.1007/s10064-018-1405-4.
  15. Momeni, E., Jahed Armaghani, D., Hajihassani, M. and Amin, M.F.M. (2015), "Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks", Measurement, 60, 50-63. https://doi.org/10.1016/j.measurement.2014.09.075.
  16. Ren, C., Yu, J., Liu, S., Yao, W., Zhu, Y. and Liu, X. (2022), "A plastic strain-induced damage model of porous rock suitable for different stress paths", Rock Mech. Rock Eng., 55(4), 1887-1906. https://doi.org/10.1007/s00603-022-02775-1.
  17. Ren, C., Yu, J., Zhang, C., Liu, X., Zhu, Y. and Yao, W. (2023a), "Micro-macro approach of anisotropic damage: A semi-analytical constitutive model of porous cracked rock", Eng. Fract. Mech., 290, 109483. https://doi.org/10.1016/j.engfracmech.2023.109483.
  18. Ren, C., Yu, J., Liu, X., Zhang, Z. and Cai, Y. (2022b), "Cyclic constitutive equations of rock with coupled damage induced by compaction and cracking", Int. J. Min. Sci. Tech., 32(5), 1153-1165. https://doi.org/10.1016/j.ijmst.2022.06.010.
  19. Shi, M., Hu, W., Li, M., Zhang, J., Song, X. and Sun, W. (2023), "Ensemble regression based on polynomial regression-based decision tree and its application in the in-situ data of tunnel boring machine", Mech. Syst. Signal Pr., 188, 110022. https://doi.org/10.1016/j.ymssp.2022.110022.
  20. Su, Y., Wang, J., Li, D., Wang, X., Hu, L., Yao, Y. and Kang, Y. (2023), "End-to-end deep learning model for underground utilities localization using GPR", Automat. Constr., 149, 104776. https://doi.org/10.1016/j.autcon.2023.104776.
  21. Shahani, N.M., Kamran, M., Zheng, X., Liu, C. and Guo, X. (2021), "Application of gradient boosting machine learning algorithms to predict uniaxial compressive strength of soft sedimentary rocks at thar coalfield", Adv. Civil Eng., 2021, Article ID 2565488, 19. https://doi.org/10.1155/2021/2565488.
  22. Singh, R., Vishal, V., Singh, T. and Ranjith, P.G. (2013), "A comparative study of generalized regression neural network approach and adaptive neuro-fuzzy inference systems for prediction of unconfined compressive strength of rocks", Neural Comput. Appl., 23, 499-506. https://doi.org/10.1007/s00521-012-0944-z.
  23. Wang, X., Li, L., Xiang, Y., Wu, Y. and Wei, M. (2024), "The influence of basalt fiber on the mechanical performance of concrete-filled steel tube short columns under axial compression", Front. Mater., 10. https://doi.org/10.3389/fmats.2023.1332269.
  24. Wang, Y., Peng, J., Wang, L., Xu, C. and Dai, B. (2023), "Micro-macro evolution of mechanical behaviors of thermally damaged rock: A state-of-the-art review", J. Rock Mech. Geotech. Eng., https://doi.org/10.1016/j.jrmge.2023.11.012.
  25. Xu, Z., Li, X., Li, J., Xue, Y., Jiang, S., Liu, L., Luo, Q., Wu, K., Zhang, N., Feng, Y., Shao, M., Jia, K. and Sun, Q. (2022), "Characteristics of source rocks and genetic origins of natural gas in deep formations, Gudian Depression, Songliao Basin, NE China", ACS Earth and Space Chem., 6(7), 1750-1771. https://doi.org/10.1021/acsearthspacechem.2c00065.
  26. Yu, J., Zhu, Y., Yao, W., Liu, X., Ren, C., Cai, Y. and Tang, X. (2021), "Stress relaxation behaviour of marble under cyclic weak disturbance and confining pressures", Measurement, 182, 109777. https://doi.org/10.1016/j.measurement.2021.109777.
  27. Yan, T., Xu, R., Sun, S.H., Hou, Z.K. and Feng, J.Y. (2023), "A real-time intelligent lithology identification method based on a dynamic felling strategy weighted random forest algorithm", Petroleum Sci., https://doi.org/10.1016/j.petsci.2023.09.011.
  28. Yao, W., Yu, J., Liu, X., Zhang, Z., Feng, X. and Cai, Y. (2023), "Experimental and theoretical investigation of coupled damage of rock under combined disturbance", Int. J. Rock Mech. Min. Sci., 164, 105355. https://doi.org/10.1016/j.ijrmms.2023.105355.
  29. Zhang, J. and Zhang, C. (2023), "Using viscoelastic materials to mitigate earthquake-induced pounding between adjacent frames with unequal height considering soil-structure interactions", Soil Dyn. Earthq. Eng., 172, 107988. https://doi.org/10.1016/j.soildyn.2023.107988.