Skip to main content

Advertisement

SpringerLink
Log in

Rock slope stability analysis and charts based on hybrid online sequential extreme learning machine model

  • Research Article
  • Published:
Earth Science Informatics Aims and scope Submit manuscript

Abstract

The stability of rock slopes is a difficult problem in the field of geotechnical and geological engineering. Less than 20% of all landslides are predictable each year, so a simple, fast, reliable and low-cost method to predict the stability of slopes is urgently needed. This study investigates a new regularized online sequential extreme learning machine, incorporated with the variable forgetting factor (FOS-ELM), based on intelligence computation to predict the factor of safety of a rock slope (F). The Bayesian information criterion (BIC) is applied to establish seven input combinations based on the parameters of the Hoek-Brown criterion and geometrical and mechanical parameters of the slope, such as the geological strength index (GSI), disturbance factor (D), rock material constant (mi), uniaxial compressive strength (σci), unit weight of the rock mass (γ), slope height (H) and slope angle (β). Seven models are established and evaluated to determine the optimal input combination. Various statistical indicators are calculated for the prediction accuracy examination. Compared to the classical extreme learning machine (ELM) model predictions of F, the results of the applied FOS-ELM model demonstrate a better prediction accuracy and are more effective when accounting for an increase in data. The FOS-ELM model with all seven input parameters is used to establish stability charts with the influence coefficient of slope angle change (ηβ), disturbance change (ηD) and slope height change (ηH). Using stability charts with a combination of ηβ, ηD and ηH can be used to quickly and preliminarily analyze rock stability as a guide for engineering practitioners in rock slope design.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig.8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Bou-Rabee M, Sulaiman SA, Saleh MS, Marafi S (2017) Using artificial neural networks to estimate solar radiation in Kuwait. Renew Sust Energ Rev 72:434–438

    Google Scholar 

  • Chen M, Lu W, Xin X, Zhao H, Bao X, Jiang X (2016) Critical geometric parameters of slope and their sensitivity analysis: a case study in jilin, northeast china. Environmental Earth Sciences 75(9):832

    Google Scholar 

  • Cho SE (2009) Probabilistic stability analyses of slopes using the ANN-based response surface. Comput Geotech 36(5):787–797

    Google Scholar 

  • Chung CJF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30(3):451–472

    Google Scholar 

  • Clausen J, Damkilde L (2008) An exact implementation of the Hoek-Brown criterion for elasto-plastic finite element calculations. Int J Rock Mech Min Sci 45(6):831–847

    Google Scholar 

  • Crisosto C, Hofmann M, Mubarak R, Seckmeyer G (2018) One-hour prediction of the global solar irradiance from all-sky images using artificial neural networks. Energies 11:2906

    Google Scholar 

  • Deng J, Lee C (2001) Displacement back analysis for a steep slope at the three gorges project site. International Journal of Rock Mechanics & Mining Sciences 38(2):259–268

    Google Scholar 

  • Deng DP, Liang L, Wang JF, Zhao LH (2016) Limit equilibrium method for rock slope stability analysis by using the generalized Hoek-Brown criterion. Int J Rock Mech Min Sci 89:176–184

    Google Scholar 

  • Eberhardt E (2012) The Hoek-Brown failure criterion. Rock mechanics and rock Engineering 45(6):981–988

    Google Scholar 

  • Erzin Y, Cetin T (2013) The prediction of the critical factor of safety of homogeneous finite slopes using neural networks and multiple regressions. Comput Geosci 51(51):305–313

    Google Scholar 

  • Fallah-Zazuli, M., Vafaeinejad, A., Alesheykh, A.A., Modiri M., Aghamohammadi H (2019) Mapping landslide susceptibility in the Zagros Mountains, Iran: a comparative study of different data mining models Earth Science Informatics,https://doi.org/10.1007/s12145-019-00389-w, 12, 615, 628

  • Gao Y, Zhang F, Lei GH, Li D, Wu Y, Zhang N (2013) Stability charts for 3d failures of homogeneous slopes. J Geotech Geoenviron 139(9):1528–1538

    Google Scholar 

  • Gao Y, Wu D, Zhang F (2015) Effects of nonlinear failure criterion on the three-dimensional stability analysis of uniform slopes. Eng Geol 198:87–93

    Google Scholar 

  • Guzzetti F, Carrara A, Cardinali M, Reichenbach P, Giardino JR, Marston D et al (1999) Landslide hazard evaluation:a review of current techniques and their application in a multi-scale study, Central Italy. Geomor-phology 31(1–4):181–216

    Google Scholar 

  • Hoek E (1994) Strength of rock and rock masses. International Society for Rock Mechanics News Journal 2(2):4–16

    Google Scholar 

  • Hoek E, Brown T, (1980) Underground excavations in rocks.London: institution of mining and metallurgy, 527

  • Hoek E, Brown T, (1988) The Hoek-Brown criterion-a 1988 update[C]// CURRAN J C ed. proceedings of the 15th Canada rock mechanics symposium. Toronto: University of Toronto, 31-38

  • Hoek E., Brown T. (2018) The Hoek-Brown failure criterion and GSI-2018 edition. Journal of Rock Mechanics and Geotechnical Engineering

  • Hoek E., Wood D., Shah S., (1992) A modified Hoek-Brown criterion for jointed rock masses[C]// HUDSON J A ed. Proceedings of the Rock Characterization, Symposium of ISRM. London: British Geotechnical Society, 209–214

  • Hoek E, Carranza-Tomes C, Corkum B. (2002) Hoek-Brown failure criterion-2002 edition.In: proceedings of the north American rock mechanics symposium Toronto

  • Hou M, Zhang TL, Weng F, Ali M, Al-Ansari N, Yaseen ZM (2018) Global solar radiation prediction using hybrid online sequential extreme learning machine model. Energies 11

  • Huang F, Huang J, Jiang S, Zhou C (2017) Landslide displacement prediction based on multivariate chaotic model and extreme learning machine. Engineering Geology 218(Complete):173–186

    Google Scholar 

  • Johari A, Mehrabani Lari A (2017) System probabilistic model of rock slope stability considering correlated failure modes. Comput Geotech 81:26–38

    Google Scholar 

  • Kumar M, Raghuwanshi N, Singh R, Wallender W, Pruitt W (2002) Estimating evapotranspiration using artificial neural network. J Irrig Drain Eng 128:224–233

    Google Scholar 

  • Li AJ, Merifield RS, Lyamin AV (2008) Stability charts for rock slopes based on the Hoek-Brown failure criterion. International Journal of Rock Mechanics & Mining Sciences 45(5):689–700

    Google Scholar 

  • Li AJ, Merifield RS, Lyamin AV (2011) Effect of rock mass disturbance on the stability of rock slopes using the Hoek-Brown failure criterion. Comput Geotech 38(4):546–558

    Google Scholar 

  • Li AJ, Khoo S, Lyamin AV, Wang Y (2016) Rock slope stability analyses using extreme learning neural network and terminal steepest descent algorithm. Autom Constr 65:42–50

    Google Scholar 

  • Li X, Zhang L, Zhang S (2018) Efficient Bayesian networks for slope safety evaluation with large quantity monitoring information. Geosci Front 9(6):1679–1687

    Google Scholar 

  • Liu Z, Shao J, Xu W, Chen H, Zhang Y (2014) An extreme learning machine approach for slope stability evaluation and prediction. Nat Hazards 73(2):787–804

    Google Scholar 

  • Liu SY, Shao LT, Li HJ (2015) Slope stability analysis using the limit equilibrium method and two finite element methods. Comput Geotech 63(63):291–298

    Google Scholar 

  • Marinos V, Marinos P, Hoek E (2005) The geological strength index: applications and limitations. Bull Eng Geol Environ 64(1):55–65

    Google Scholar 

  • Paleologu C, Benesty J, Ciochina S (2008) A robust variable forgetting factor recursive least-squares algorithm for system identification. IEEE Signal Processing Letters 15:597–600

    Google Scholar 

  • Priest SD (2005) Determination of shear strength and three-dimensional yield strength for the Hoek-Brown criterion. Rock Mech Rock Eng 38(4):299–327

    Google Scholar 

  • Qian ZG, Li AJ, Lyamin AV, Wang CC (2017) Parametric studies of disturbed rock slope stability based on finite element limit analysis methods. Comput Geotech 81:155–166

    Google Scholar 

  • Saade A, Aboujaoude G, Wartman J (2016) Regional-scale co-seismic landslide assessment using limit equilibrium analysis. Eng Geol 204:53–64

    Google Scholar 

  • Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6:461–464

    Google Scholar 

  • Shafizadeh-Moghadam H, Minaei M, Shahabi H, Hagenauer J (2019) Big data in Geohazard; pattern mining and large scale analysis of landslides in Iran. Earth Sci Inf 12:1–17

    Google Scholar 

  • Siad L (2003) Seismic stability analysis of fractured rock slopes by yield design theory. Soil Dyn Earthq Eng 23(3):21–30

    Google Scholar 

  • Sinha S, Singh TN, Singh VK, Verma AK (2010) Epoch determination for neural network by self-organized map (SOM). Comput Geosci 14(1):199–206

    Google Scholar 

  • Sonmez H, Ulusay R (1999) Modifications to the geological strength index (GSI) and their applicability to stability of slopes. Int J Rock Mech Min Sci 36(6):743–760

    Google Scholar 

  • Steward T, Sivakugan N, Shukla SK, Das BM (2010) Taylor’s slope stability charts revisited. International Journal of Geomechanics 11(4):348–352

    Google Scholar 

  • Sun D, Chen Z, Du B, Cao Y (1997) Modifications to the RMR-SMR system for slope stability evaluation. Chinese Journal of Rock Mechanics and Engineering,16(4), 297–304

  • Taylor DW (1937) Stability of earth slopes.Journal of the. Boston Society of Civil Engineers 24(3):197–246

    Google Scholar 

  • Wang Y, Cao Z, Au SK (2010) Efficient Monte Carlo simulation of parameter sensitivity in probabilistic slope stability analysis. Comput Geotech 37(7):1015–1022

    Google Scholar 

  • Wenkai F, Shan D, Qi W, Xiaoyu Y, Zhigang L, Huilin B (2018) Improving the Hoek-Brown criterion based on the disturbance factor and geological strength index quantification. Int J Rock Mech Min Sci 108:96–104

    Google Scholar 

  • Xu J, Yang X (2018) Seismic stability analysis and charts of a 3d rock slope in Hoek-Brown media. Int J Rock Mech Min Sci 112:64–76

    Google Scholar 

  • Yun, L., Keping, Z., Jielin, L. (2018) Prediction of slope stability using four supervised learning methods. IEEE Access,1–1

  • Zanbak C (1983) Design charts for rock slopes susceptible to toppling. J Geotech Eng ASCE 190(8):1039–1062

    Google Scholar 

  • Zhang Z, Liu Z, Zheng L, Zhang Y (2014) Development of an adaptive relevance vector machine approach for slope stability inference. Neural Comput & Applic 25(7–8):2025–2035

    Google Scholar 

  • Zhao J (2000) Applicability of Mohr-coulomb and Hoek-Brown strength criteria to the dynamic strength of brittle rock. International Journal of Rock Mechanics & Mining Sciences 37(7):1115–1121

    Google Scholar 

Download references

Acknowledgements

This project was supported by the Fundamental Research Funds for the Central Universities of Central South University (2017zzts178; 2018zzts322);.

Author information

Authors and Affiliations

  1. School of Geo-sciences and Info-Physics, Central South University, Changsha, Hu Nan, 410083, People’s Republic of China

    Chao Deng, Huanxiao Hu & Jiale Chen

  2. Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, Changsha, Hu Nan, 410083, People’s Republic of China

    Huanxiao Hu

  3. School of Mathematics and Statistics, Central South University, Changsha, Hu Nan, 410083, People’s Republic of China

    Tianle Zhang

Authors
  1. Chao Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huanxiao Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tianle Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiale Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chao Deng.

Additional information

Communicated by: H. Babaie

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Deng, C., Hu, H., Zhang, T. et al. Rock slope stability analysis and charts based on hybrid online sequential extreme learning machine model. Earth Sci Inform 13, 729–746 (2020). https://doi.org/10.1007/s12145-020-00458-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12145-020-00458-5

Keywords

Search

Navigation