Abstract
Accurately determining geomechanical parameters and rock mass deformation are key components of slope stability assessment as well as structural design and safe construction. Numerical models are therefore commonly applied to these issues as a component of back analysis, which provides a practical tool for determining geomechanical parameters in rock engineering. Although once quantified, rock mass deformation can also be calculated using an appropriate numerical model, the traditional approaches tend to be time-consuming and cannot be used to accurately determine deformation uncertainty in practical rock engineering. A relevance vector machine (RVM) was adopted in this study to build the complex mapping relationship between geomechanical parameters and slope deformation, while an artificial bee colony (ABC) was also utilized to search for optimal parameters based on the RVM model and displacement monitored in the field. On this basis, both deformation and associated uncertainty can be predicted using this model via the input of determined geomechanical parameters. The method advocated in this paper was then applied to a rock slope at the Longtan hydropower station, China; results demonstrate that the RVM model accurately describes the relationship between geomechanical parameters and displacement increments and can thus be used to predict both deformation and associated uncertainty.
Similar content being viewed by others
References
Bertuzzi R (2017) Back-analysing rock mass modulus from monitoring data of two tunnels in Sydney. Australia J Rock Mech Geotech 9(5):877–891. https://doi.org/10.1016/j.jrmge.2017.05.005
Callisto L, Ricci C (2019) Interpretation and back-analysis of the damage observed in a deep tunnel after the 2016 Norcia earthquake in Italy. Tunn Undergr Sp Tech 89:238–248. https://doi.org/10.1016/j.tust.2019.04.012
Deng JH, Lee CF (2001) Displacement back analysis for a steep slope at the Three Gorges Project site. Int J Rock Mech Min 38(2):259–268. https://doi.org/10.1016/S1365-1609(00)00077-0
Dong JJ, Tung YH, Chen CC, Liao JJ, Pan YW (2011) Logistic regression model for predicting the failure probability of a landslide dam. Eng Geol 117(1–2):52–61. https://doi.org/10.1016/j.enggeo.2010.10.004
Feng XT, Zhao HB, Li SJ (2004a) A new displacement back analysis to identify mechanical geomaterial parameters based on hybrid intelligent methodology. Int J Numer Anal Met 28(11):1141–1165. https://doi.org/10.1002/nag.381
Feng XT, Zhao HB, Li SJ (2004b) Modeling non-linear displacement time series of geo-materials using evolutionary support vector machine. Int J Rock Mech Min 41(7):1087–1107. https://doi.org/10.1016/j.ijrmms.2004.04.003
Ferrero AM, Migliazza M, Segalini A, Gullì D (2013) In-situ stress measurement interpretations in a large underground marble quarry by 3D modeling. Int J Rock Mech Min 60:103–113. https://doi.org/10.1016/j.ijrmms.2012.12.008
Gao W (2006) Study on displacement prediction of landslide based on grey system and evolutionary neural network. In: Computational methods in engineering & science. Springer, Berlin, Heidelberg. pp. 252–275. https://doi.org/10.1007/978-3-540-48260-4-121
Ghorbani E, Moosavi M, Hossaini MF et al (2021) Determination of initial stress state and rock mass deformation modulus at Lavarak HEPP by back analysis using ant colony optimization and multivariable regression analysis. Bull Eng Geol Environ 80:429–442. https://doi.org/10.1007/s10064-020-01936-2
Gioda G, Jurina L (1981) Numerical identification of soil structure interaction pressures. Int J Numer Anal Met 5:33–56. https://doi.org/10.1002/nag.1610050105
Huang J, Griffiths D (2009) Return mapping algorithms and stress predictors for failure analysis in geomechanics. J Eng Mech 135(4):276–284. https://doi.org/10.1061/(ASCE)0733-9399(2009)135:4(276)
Huang F, Huang J, Jiang S, Zhou C (2017) Landslide displacement prediction based on a multivariate chaotic model and extreme learning machine. Eng Geol 218:173–186. https://doi.org/10.1016/j.enggeo.2017.01.016
Jibson RW (2007) Regression models for estimating coseismic landslide displacement. Eng Geol 91(2):209–218. https://doi.org/10.1016/j.enggeo.2007.01.013
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization, Technical Report-TR06. Erciyes University, Engineering Faculty, Computer Engineering Department
Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697. https://doi.org/10.1016/j.asoc.2007.05.007
Karaboga D, Ozturk C, (2011) A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Applied Soft Computing 11 (1):652-657
Li S, Zhao H, Ru Z, Sun Q, Li J (2016) Identifying geomechanical parameters of high cut rock slopes by an improved multi-output support vector machine method. Environ Earth Sci 75(8):673–686. https://doi.org/10.1007/s12665-016-5491-y
Newmark NM (1965) Effects of earthquakes on dams and embankments. Geotechnique 15(2):139–159
Oreste P (2005) Back analysis techniques for the improvement of the understanding of rock in underground constructions. Tunn Undergr Sp Tech 20(1):7–21. https://doi.org/10.1016/j.tust.2004.04.002
Pichler B, Lackner R, Mang HA (2003) Back analysis of model parameters in geotechnical engineering by means of soft computing. Int J Numer Meth Eng 57(14):1943–1978. https://doi.org/10.1002/nme.740
Pradel D, Smith PM, Stewart JP, Raad G (2005) Case history of landslide movement during the Northridge earthquake. J Geotech Geoenviron 131(11):1360–1369. https://doi.org/10.1061/(ASCE)1090-0241(2005)131:11(1360)
Romeo R (2000) Seismically induced landslide displacements: a predictive model. Eng Geol 58(3):337–351. https://doi.org/10.1016/S0013-7952(00)00042-9
Sakurai S (1987) Interpretation of the results of displacement measurements in cut slopes. In: Proceedings of the Second International Symposium on FMGM87, Kobe, 2, pp.528–2540
Sakurai S, Dees WN, Shinji M (1986) Back analysis for determining material characteristics in cut slopes. Proceedings of the International Symposium on ECRF, Beijing, pp.770–776
Sakurai S, Takeuchi K (1983) Back analysis of measured displacements of tunnels. Rock Mech and Rock Eng 16:173–180
Tian H, Chen W, Yang D, Dai Y, Yang J (2015) Application of the orthogonal design method in geotechnical parameter back analysis for underground structures. Bull Eng Geol Environ 75:239–249. https://doi.org/10.1007/s10064-015-0730-0
Tipping M (2001) Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res 1(3):211–244. https://doi.org/10.1162/15324430152748236
Ye KQ (1998) Orthogonal column Latin hypercubes and their application in computer experiments. J Am Stat Assoc 93(444):1430–1438. https://doi.org/10.2307/2670057
Yu YZ, Zhang BY, Yuan HN (2007) An intelligent displacement back-analysis method for earth-rockfill dams. Comput Geotech 34(6):423–434. https://doi.org/10.1016/j.compgeo.2007.03.002
Zhang S, Yin S (2013) Reservoir geomechanical parameters identification based on ground surface movements. Acta Geotech 8(3):279–292. https://doi.org/10.1007/s11440-012-0196-1
Zhao HB, Chen BR (2021) Inverse analysis for rock mechanics based on a high dimensional model representation. Inverse Probl Sci En. https://doi.org/10.1080/17415977.2020.1870972
Zhao HB, Zl Ru, Yin SD (2015) A practical indirect back analysis approach for geomechanical parameters identification. Mar Georesour Geotec 33(3):212–221. https://doi.org/10.1080/1064119X.2013.836258
Zhao H, Ru Z, Yin S (2012) Relevance vector machine applied to slope stability analysis. Int J Numer Anal Met 36(5):643–652. https://doi.org/10.1002/nag.1037
Zhao HB, Yin SD (2009) Geomechanical parameters identification by particle swarm optimization and support vector machine. Appl Math Model 33(10):3997–4012. https://doi.org/10.1016/j.apm.2009.01.011
Funding
The authors were supported by the Open Research Fund of State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences (Grant No. Z020006), and the National Natural Science Foundation of China (Grant No. U1765206).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Rights and permissions
About this article
Cite this article
Zhao, H., Li, S. Determining geomechanical parameters and a deformation uncertainty analysis of the Longtan Hydropower Station slope, China. Bull Eng Geol Environ 80, 6429–6443 (2021). https://doi.org/10.1007/s10064-021-02339-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10064-021-02339-7