Geomechanics and Engineering

  • 제28권3호
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  • Pages.313-324
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  • 2022
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  • 2005-307X(pISSN)
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  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Prediction of the static and dynamic mechanical properties of sedimentary rock using soft computing methods

  • Lawal, Abiodun I. (Department of Energy Resources Engineering, Inha University Yong-Hyun Dong) ;
  • Kwon, Sangki (Department of Energy Resources Engineering, Inha University Yong-Hyun Dong) ;
  • Aladejare, Adeyemi E. (Oulu Mining School, University of Oulu) ;
  • Oniyide, Gafar O. (Department of Mining Engineering, Federal University of Technology)
  • 투고 : 2021.07.11
  • 심사 : 2021.12.13
  • 발행 : 2022.02.10
⟨ 이전 논문 다음 논문 ⟩

초록

Rock properties are important in the design of mines and civil engineering excavations to prevent the imminent failure of slopes and collapse of underground excavations. However, the time, cost, and expertise required to perform experiments to determine those properties are high. Therefore, empirical models have been developed for estimating the mechanical properties of rock that are difficult to determine experimentally from properties that are less difficult to measure. However, the inherent variability in rock properties makes the accurate performance of the empirical models unrealistic and therefore necessitate the use of soft computing models. In this study, Gaussian process regression (GPR), artificial neural network (ANN) and response surface method (RSM) have been proposed to predict the static and dynamic rock properties from the P-wave and rock density. The outcome of the study showed that GPR produced more accurate results than the ANN and RSM models. GPR gave the correlation coefficient of above 99% for all the three properties predicted and RMSE of less than 5. The detailed sensitivity analysis is also conducted using the RSM and the P-wave velocity is found to be the most influencing parameter in the rock mechanical properties predictions. The proposed models can give reasonable predictions of important mechanical properties of sedimentary rock.

키워드

과제정보

This work was supported by Korea Research Fellowship Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (2019H1D3A1A01102993) and Inha University Research Grant (2021).

참고문헌

  1. Aboutaleb, S., Behnia, M., Bagherpour, R. and Bluekian, B. (2018), "Using non-destructive tests for estimating uniaxial compressive strength and static Young's modulus of carbonate rocks via some modeling techniques", Bull. Eng. Geol. Environ., 77(4), 1717-1728. https://doi.org/10.1007/s10064-017-1043-2.
  2. Aladejare, A.E. (2020), "Evaluation of empirical estimation of uniaxial compressive strength of rock using measurements from index and physical tests", J. Rock Mech. Geotech. Eng., 12(2), 256-268. https://doi.org/10.1016/j.jrmge.2019.08.001.
  3. Aladejare, A.E., Karenlamp, K. and Lawal, A.I. (2020), "Application of Artificial Intelligence for Characterization of Rocks from Otanmaki, Finland", Proceedings of the 54th US Rock Mechanics/Geomechanics Symposium. American Rock Mechanics Association.
  4. Aladejare, A.E., Alofe, E.D., Onifade, M., Lawal, A.I., Ozoji, T.M. and Zhang, Z.-X. (2021), "Empirical estimation of uniaxial compressive strength of rock: database of simple, multiple, and artificial intelligence-based regressions", Geotech. Geol. Eng., 39, 4427-4455. https://doi.org/10.1007/s10706-021-01772-5.
  5. Aliyu, M.M., Shang, J., Murphy, W., Lawrence, J.A., Collier, R., Kong, F. and Zhao, Z. (2019), "Assessing the uniaxial compressive strength of extremely hard cryptocrystalline flint", Int. J. Rock Mech. Min. Sci., 113, 310-321. https://doi.org/10.1016/j.ijrmms.2018.12.002.
  6. Armaghani, D.J., Mohamad, E.T., Momeni, E., Narayanasamy, M. S. (2015), "An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young's modulus: A study on Main Range granite", Bull Eng Geol Environ, 74(4):1301-1319. https://doi.org/10.1007/s10064-014-0687-4.
  7. Armaghani, D.J., Mohamad, E.T., Hajihassani, M., Yagiz, S. and Motaghedi, H. (2016), "Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances", Eng. Comput., 32(2), 189-206. https://doi.org/10.1007/s00366-015-0410-5.
  8. Armaghani, D.J., Safari, V., Fahimifar, A., Monjezi, M. and Mohammadi, M.A. (2018), "Uniaxial compressive strength prediction through a new technique based on gene expression programming", Neural Comput. Appl., 30(11), 3523-3352. https://doi.org/10.1007/s00521-017-2939-2.
  9. Armaghani, D.J., Mamou, A., Maraveas, C., Roussis, P.C., Siorikis, V.G., Skentou, A.D. and Asteris, P.G. (2021), "Predicting the unconfined compressive strength of granite using only two nondestructive test indexes", Geomech. Eng., 25(4), 317-330. https://doi.org/10.12989/gae.2021.25.4.317.
  10. Azimian, A., Ajalloeian, R. and Fatehi, L. (2014), "An empirical correlation of uniaxial compressive strngth with P-wave velocity and point load strength index on marly rocks using statistical method", J. Geotech. Geol. Eng., 32, 205-214. https://doi.org/10.1007/s10706-013-9703-x.
  11. Ashtari, M., Mousavi, S.E., Cheshomi, A. and Khamechian, M. (2019), "Evaluation of the single compressive strength test in estimating uniaxial compressive and Brazilian tensile strengths and elastic modulus of marlstone", Eng. Geol., 248, 256-266. https://doi.org/10.1016/j.enggeo.2018.12.005.
  12. Bisoyi, S.K. and Pal, B.K. (2020), "Prediction of ground vibration using various regression analysis", J. Min. Sci., 56(3), 378-387. https://doi.org/10.1134/S1062739120036665.
  13. Dincer, I., Acar, A. and Ural, S. (2008), "Estimation of strength and deformation properties of Quaternary caliche deposits", Bull. Eng. Geol. Environ., 63, 141-148. https://doi.org/10.1007/s10064-008-0146-1.
  14. Heidari, M., Mohseni, H. and Jalali, S.H. (2018), "Prediction of Uniaxial Compressive Strength of Some Sedimentary Rocks by Fuzzy and Regression Models", Geotech. Geol. Eng., 36, 401-412. https://doi.org/10.1007/s10706-017-0334-5.
  15. International Society for Rock Mechanics (ISRM) (1981), Suggested methods. Rock characterization testing and monitoring, E. T. Brown, ed., Pergamon, Oxford, U.K.
  16. Jalali, S.H., Heidari, M. and Mohseni, H. (2017), "Comparison of models for estimating uniaxial compressive strength of some sedimentary rocks from Qom Formation", Environ. Earth Sci., 76, 753. https://doi.org/10.1007/s12665-017-7090-y.
  17. Jamshidi, A., Zamanian, H., Sahamieh, R.Z. (2018), "The effect of density and porosity on the correlation between uniaxial compressive strength and P-wave velocity", Rock Mech. Rock Eng., 51, 1279-1286. https://doi.org/10.1007/s00603-017-1379-8.
  18. Khandelwal, M. and Singh, T.N. (2009), "Correlating static properties of coal measures rocks with P-wave velocity", Int. J. Coal Geol., 79(1-2), 55-60. https://doi.org/10.1016/j.coal.2009.01.004.
  19. Koken, E. and Lawal, A.I. (2021), "Investigating the effects of feeding properties on rock breakage by jaw crusher using response surface method and gene expression programming", Adv Powd Tech., 32, 1521-1531. https://doi.org/10.1016/j.apt.2021.03.007.
  20. Kowalski S.M. and Montgomery D.C. (2011), Design and analysis of experiments, Minitab manual. 7th Ed., Wiley, ISBN: 978-0-470-16990-2.
  21. Lawal, A.I. and Idris, M.A. (2019), "An artificial neural networkbased mathematical model for the prediction of blast-induced ground vibrations", Int. J. Environ Std., 77(2), 318-334. https://doi.org/10.1080/00207233.2019.1662186.
  22. Lawal, A.I. and Kwon, S. (2020), "Application of artificial intelligence in rock mechanics: an overview", J. Rock Mech. Geotech. Eng., 13, 248-266. https://doi.org/10.1016/j.jrmge.2020.05.010.
  23. Lawal, A.I. (2020), "An artificial neural network-based mathematical model for the prediction of blast-induced ground vibration in granite quarries in Ibadan, Oyo State, Nigeria", Sci. African, 8, e00413. https://doi.org/10.1016/j.sciaf.2020.e00413.
  24. Lawal, A.I., Kwon, S. and Kim, G.Y. (2021a), "Prediction of the blast-induced ground vibration in tunnel blasting using ANN, metaheuristic ANNs and gene expression programming". Acta Geophys., 69, 161-174. https://doi.org/10.1007/s11600-020-00532-y.
  25. Lawal, A.I., Kwon, S., Hammed, O.S. and Idris, M.A. (2021b), "Blast-induced ground vibration prediction in granite quarries: An application of Gene expression programming, ANFIS, and Sine Cosine algorithm optimized ANN", Int. J. Mining Sci. Tech., 31, 265-277. https://doi.org/10.1016/j.ijmst.2021.01.007.
  26. Lawal, A.I., Olajuyi, S.I., Kwon, S., Aladejare E.A. and Edo, T.M. (2021c), "Prediction of blast-induced ground vibration using GPR and blast-design parameters optimization based on novel grey-wolf optimization algorithm", Acta Geophys., 69, 1313-1324. https://doi.org/10.1007/s11600-021-00607-4.
  27. Lawal, A.I., Oniyide, G.O., Kwon, S., Onifade, M., Koken, E. and Ogunsola, N.O. (2021d), "Prediction of mechanical properties of coal from non-destructive properties: A comparative application of MARS, ANN, and GA", Nat. Resour. Res., 30, 4547-4563. https://doi.org/10.1007/s11053-021-09955-w.
  28. Luat, N.V., Lee, K. and Thai, D.K. (2020), "Application of artificial neural networks in settlement prediction of shallow foundations on sandy soils", Geomech. Eng., 20(5), 385-397. http://dx.doi.org/10.12989/gae.2020.20.5.385.
  29. Majdi, A. and Rezaei, M. (2013), "Prediction of unconfined compressive strength of rock surrounding a roadway using artificial neural network", Neural Comput. Appl., 23, 381-389. https://doi.org/10.1007/s00521-012-0925-2.
  30. Majdi, A. and Beiki, M. (2010), "Evolving neural network using a genetic algorithm for predicting the deformation modulus of rock masses", Int. J. Rock Mech. Min. Sci., 47(2), 246-253. https://doi.org/10.1016/j.ijrmms.2009.09.011.
  31. Mohamad, E.T., Armaghani, D.J., Momeni, E. and Abad S. (2015), "Prediction of the unconfined compressive strength of soft rocks: A PSO-based ANN approach", Bull. Eng. Geol. Environ., 74(3), 745-757. https://doi.org/10.1007/s10064-014-0638-0.
  32. Mohamad, E.T., Armaghani, D.J., Momeni, E., Yazdavar, A.H. and Ebrahimi, M. (2018), "Rock strength estimation: a PSO-based BP approach", Neural Comput. Appl., 30(5), 1635-1646. https://doi.org/10.1007/s00521-016-2728-3.
  33. Moradian, Z.A. and Behnia, M. (2009), "Predicting the Uniaxial Compressive Strength and Static Young's Modulus of Intact Sedimentary Rocks Using the Ultrasonic Test", Int. J. Geomech., 9(1), 14-19. https://doi.org/10.1061/(ASCE)1532-3641(2009)9:1(14).
  34. Myers, R.H. (2016), Response surface methodology: Process and product optimization using designed experiments, 4th ed. (Raymond H. Myers, Douglas C. Montgomery, Christine M. Anderson-Cook. Eds.), Wiley, ISBN 978-1-118-91601-8.
  35. Rasmussen, C.E. and Williams, C.K.I (2006), Gaussian processes for machine learning, The MIT Press, Cambridge.
  36. Ren, Q., Wang, G., Li, M. and Han, S. (2019), "Prediction of rock compressive strength using machine learning algorithms based on spectrum analysis of geological hammer". Geotech. Geol. Eng., 37, 475-489. https://doi.org/10.1007/s10706-018-0624-6.
  37. Sharma, L.K., Vishal, V. and Singh, T.N. (2017), "Developing novel models using neural networks and fuzzy systems for the prediction of strength of rocks from key geomechanical properties", Meas., 102, 158-169. https://doi.org/10.1016/j.measurement.2017.01.043.
  38. Taylor, K.E. (2001), "Summarizing multiple aspects of model performance in a single diagram", J. Geophys. Res., 106, 7183-7192. https://doi.org/10.1029/2000JD900719.
  39. Torabi, S.R., Ataei, M. and Javanshir, M. (2010), "Application of Schmidt rebound number for estimating rock strength under specific geological conditions", J. Min. Environ, 1(2), 1-8. https://doi.org/10.22044/jme.2011.9.
  40. Uyanik, O., Sabbag, N., Uyanik, N.A. and Oncu, Z. (2019), "Prediction of mechanical and physical properties of some sedimentary rocks from ultrasonic velocities". Bull. Eng. Geol. Environ., 78, 6003-6016. https://doi.org/10.1007/s10064-019-01501-6.
  41. Yilmaz, I. and Yuksek, A.G. (2018), "An example of artificial neural network (ANN) application for indirect estimation of rock parameters", Rock Mech. Rock Eng., 41(5), 781-795. https://doi.org/10.1007/s00603-007-0138-7.
  42. Zorlu, K., Gokceoglu, C., Ocakoglu, F., Nefeslioglu, H.A. and Acikalin, S. (2008), "Prediction of uniaxial compressive strength of sandstones using petrography-based models", Eng. Geol., 96(3-4), 141-158. https://doi.org/10.1016/j.enggeo.2007.10.009.