Skip to main content

Advertisement

SpringerLink
Log in

Peak shear strength prediction for discontinuities between two different rock types using a neural network approach

  • Original Paper
  • Published:
Bulletin of Engineering Geology and the Environment Aims and scope Submit manuscript

Abstract

The peak shear strength of discontinuities between two different rock types is essential to evaluate the stability of a rock slope with interlayered rocks. However, current research has paid little attention to shear strength parameters of discontinuities with different joint wall compressive strength (DDJCS). In this paper, a neural network methodology was used to predict the peak shear strength of DDJCS considering the effect of joint wall strength combination, normal stress and joint roughness. The database was developed by laboratory direct shear tests on artificial joint specimens with seven different joint wall strength combinations, four designed joint surface topographies and six types of normal stresses. A part of the experimental data was used to train a back-propagation neural network model with a single-hidden layer. The remaining experimental data was used to validate the trained neural network model. The best geometry of the neural network model was determined by the trial-and-error method. For the same data, multivariate regression analysis was also conducted to predict the peak shear strength of DDJCS. Prediction precision of the neural network model and multivariate regression model was evaluated by comparing the predicted peak shear strength of DDJCS with experimental data. The results showed that the capability of the developed neural network model was strong and better than the multivariate regression model. Finally, the established neural network model was applied in the stability evaluation of a typical rock slope with DDJCS as the critical surface in the Badong formation of China.

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.

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

Similar content being viewed by others

References

  • Adoko AC, Jiao YY, Wu L, Wang H, Wang ZH (2013) Predicting tunnel convergence using multivariate adaptive regression spline and artificial neural network. Tunn Undergr Space Technol 38:368–376

    Article  Google Scholar 

  • Alemdag S, Gurocak Z, Cevik A, Cabalar A, Gokceoglu C (2016) Modeling deformation modulus of a stratified sedimentary rock mass using neural network, fuzzy inference and genetic programming. Eng Geol 203:70–82

    Article  Google Scholar 

  • Alshihri MM, Azmy AM, El-Bisy MS (2009) Neural networks for predicting compressive strength of structural light weight concrete.Construction and. Building Materials 23(6):2214–2219

    Article  Google Scholar 

  • Atapour H, Moosavi M (2014) The influence of shearing velocity on shear behavior of artificial joints. Rock Mech Rock Eng 47(5):1745–1761

    Article  Google Scholar 

  • Barton N (1973) Review of a new shear strength criterion for rock joints.Engineering. Geology 7(4):287–332

    Google Scholar 

  • Baum EB, Haussler D (1989) What size net gives valid generalization? Neural Comput 1(1):151–160

    Article  Google Scholar 

  • Beiki M, Bashari A, Majdi A (2010) Genetic programming approach for estimating the deformation modulus of rock mass using sensitivity analysis by neural network. Int J Rock Mech Min Sci 47(7):1091–1103

    Article  Google Scholar 

  • Chok YH, Jaksa MB, Kaggwa WS, Griffiths DV, Fenton GA (2016) Neural network prediction of the reliability of heterogeneous cohesive slopes. Int J Numer Anal Methods Geomech 40(11):1556–1569

    Article  Google Scholar 

  • Choobbasti AJ, Farrokhzad F, Barari A (2009) Prediction of slope stability using artificial neuralnetwork (case study: Noabad, Mazandaran, Iran). Arab J Geosci 2(4):311–319

    Article  Google Scholar 

  • Cottrell B (2009) Updates to the GG-shear strength criterion. Dissertation, University of Toronto

  • Chua CG, Goh ATC (2005) Estimating wall deflections in deep excavations using Bayesian neural networks. Tunn Undergr Space Technol 20(4):400–409

    Article  Google Scholar 

  • Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Control Signals Syst 2(4):303–314

    Article  Google Scholar 

  • Draper NR, Smith H (1981) Applied regression analysis. John Wiley and Sons, New York

    Google Scholar 

  • Ebrahimi E, Monjezi M, Khalesi MR, Armaghani DJ (2016) Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm. Bull Eng Geol Environ 75(1):27–36

    Article  Google Scholar 

  • Fang K, Wu Q, Wang J, Tan FL (2014) Research on shear characteristics and the evolution mechanism of bedding planes between two different rock types based on particle flow code. Journal of Yangtze River Scientific Research Institute 31(11):31–37 (in Chinese with English Abstract)

    Google Scholar 

  • Garaga A, Latha GM (2010) Intelligent prediction of the stress–strain response of intact and jointed rocks. Comput Geotech 37(5):629–637

    Article  Google Scholar 

  • García-Pérez J, Riaño R (2016) Optimum seismic zoning using an artificial neural network. Earthquake Spectra 32(2):1187–1207

    Article  Google Scholar 

  • Ge ZX, Sun ZQ (2007) Neural network theory and MATLAB R2007 application. Publishing House of Electronics Industry, Beijing

    Google Scholar 

  • Ghazvinian AH, Taghichian A, Hashemi M, Mar’ashi SA (2010) The shear behavior of bedding planes of weakness between two different rock types with high strength difference. Rock Mech Rock Eng 43(1):69–87

    Article  Google Scholar 

  • Gokceoglu C, Sonmez H, Kayabasi A (2003) Predicting the deformation moduli of rockmasses. Int J Rock Mech Min Sci 40(5):701–710

    Article  Google Scholar 

  • Grasselli G, Egger P (2003) Constitutive law for the shear strength of rock joints based on three-dimensional surface parameters. Int J Rock Mech Min Sci 40(1):25–40

    Article  Google Scholar 

  • Hajihassani M, Armaghani DJ, Marto A, Mohamad ET (2015) Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bull Eng Geol Environ 74(3):873–886

    Article  Google Scholar 

  • Hencher SR, Richards LR (2015) Assessing the shear strength of rock discontinuities at laboratory and field scales. Rock Mech Rock Eng 48(3):883–905

    Article  Google Scholar 

  • Huang SM, Wang YY, Ding HM et al. (2016) Norm for seismic design of buildings. China Architecture & Building Press

  • Jang HS, Jang BA (2015) New method for shear strength determination of unfilled, unweathered rock joint. Rock Mech Rock Eng 48(4):1515–1534

    Article  Google Scholar 

  • Khaw JFC, Lim BS, Lim LEN (1995) Optimal design of neural networks using the Taguchi method. Neurocomputing 7(3):225–245

    Article  Google Scholar 

  • Kulatilake PHSW, Wu Q, Hudaverdi T, Kuzu C (2010) Mean particle size prediction in rock blast fragmentation using neural networks. Eng Geol 114(3):298–311

    Article  Google Scholar 

  • Kulatilake PHSW, Shou G, Huang TH, Morgan RM (1995) New peak shear strength criteria for anisotropic rock joints. International Journal of Rock Mechanics and Mining Sciences &Geomechanics Abstracts 32(7):673–697

    Article  Google Scholar 

  • Kung TC, Hsiao CL, Schuster M, Juang CH (2007) A neural network approach to estimating excavation-induced wall deflection in soft clays. Comput Geotech 34:385–396

    Article  Google Scholar 

  • Ladanyi B, Archambault G (1977) Shear strength and deformability of filled indented joints. In: International symposium on the geotechnics of structurally complex formations, Capri, pp317–326

  • Li CD, Tang HM, Ge YF, Hu XL, Wang LQ (2014) Application of back-propagation neural network on bank destruction forecasting for accumulative landslides in the three gorges reservoir region, China. Stoch Env Res Risk A 28(6):1465–1477

    Article  Google Scholar 

  • Lian C, Zeng ZG, Yao W, Tang HM (2015) Multiple neural networks switched prediction for landslide displacement. Eng Geol 186:91–99

    Article  Google Scholar 

  • Mahdevari S, Torabi SR (2012) Prediction of tunnel convergence using artificial neural networks. Tunn Undergr Space Technol 28:218–228

    Article  Google Scholar 

  • Maier HR, Dandy GC (1998) The effect of internal parameters and geometry on the performance of back-propagation neural networks: an empirical study. Environ Model Softw 13(2):193–209

    Article  Google Scholar 

  • Majdi A, 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

    Article  Google Scholar 

  • Maji VB, Sitharam TG (2008) Prediction of elastic modulus of jointed rock mass using artificial neural networks. Geotech Geol Eng 26(4):443–452

    Article  Google Scholar 

  • Maksimovic M (1992) Newdescription of shear strength for rockjoints. Rock Mech Rock Eng 25(4):275–284

    Article  Google Scholar 

  • Masters T (1993) Practical neural network recipes in C ++. Morgan Kaufmann, SanDiego

    Google Scholar 

  • Mirzaghorbanali A, Nemcik J, Aziz N (2014) Effects of cyclic loading on the shear behaviour of infilled rock joints under constant normal stiffness conditions. Rock Mech Rock Eng 47(4):1373–1391

    Article  Google Scholar 

  • Montgomery DC, Peck EA, Vining GG (2006) Introduction to linear RegressionAnalysis. John Wiley & Sons Inc, New Jersey

    Google Scholar 

  • Ochmański M, Modoni G, Bzówka J (2015) Prediction of the diameter of jet grouting columns with artificial neural networks. Soils Found 55(2):425–436

    Article  Google Scholar 

  • Papaliangas TT, Hencher SR, Lumsden AC (1995) A comprehensive peakshearstrength criterion for rock joints. In: Fuji T (ed) Proceedings 8th international congress on rock mechanics. Balkema, Tokyo, pp 359–366

    Google Scholar 

  • Patton FD (1966) Multiple modes of shear failure in rock. In: Proceedings of the 1st congress of ISRM. Lisboa, Portugal, pp 509–513

    Google Scholar 

  • Polykretis C, Ferentinou M, Chalkias C (2015) A comparative study of landslide susceptibility mapping using landslide susceptibility index and artificial neural networks in the Krios River and Krathis River catchments (northern Peloponnesus, Greece). Bull Eng Geol Environ 74(1):27–45

    Article  Google Scholar 

  • Rogers LL, Dowla FU (1994) Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling. Water Resour Res 30(2):457–481

    Article  Google Scholar 

  • Singh PK, Tripathy A, Kainthola A, Mahanta B, Singh V, Sing TN (2017) Indirect estimation of compressive and shear strength from simple index tests. Eng Comput 33:1–11

    Article  Google Scholar 

  • Sonmez H, Gokceoglu C, Nefeslioglu HA, Kayabasi A (2006) Estimation of rock modulus: for intact rocks with an artificial neural network and for rock masses with a new empirical equation. Int J Rock Mech Min Sci 43(2):224–235

    Article  Google Scholar 

  • Sow D, Rivard P, Peyras L, Breul P, Moradian ZA, Bacconnet C, Ballivy G (2015) Comparison of joint shearing resistance obtained with the barton and choubey criterion and with direct shear tests. Rock Mech Rock Eng 49(8):3357–3361

    Article  Google Scholar 

  • Tang ZC, Jiao YY, Wong LNY, Wang XC (2016) Choosing appropriate parameters for developing empirical shear strength criterion of rock joint: review and new insights. Rock Mech Rock Eng 49(11):4479–4490

    Article  Google Scholar 

  • Tang ZC, Wong LNY (2016) Influences of normal loading rate and shear velocity on the shear behavior of artificial rock joints.Rock. Mechanics and Rock Engineering 49(6):2165–2172

    Article  Google Scholar 

  • Widrow B (1987) Adaline and madaline 1963: Plenary speech. In: Proceedings of 1st IEEE international conference on neural networks, SanDiego, pp 143–158

  • Wu YW (2012) Software engineering and knowledge engineering: theory and practice. Springer Science & Business Media

  • Xie H, Pariseau WG(1992) Fractal estimation of the joint roughness coefficients. In:Proceedings of international conference on fractured and jointed rock masses, Balkema, Rotterdam, pp 125–131

Download references

Acknowledgements

The research was funded by the National Key R&D Program of China (2017YFC1501301) and Natural Science Foundation of Hubei province of China (2018CFB666). The authors are grateful to the organization s that provided the aforementioned financial support.

Author information

Authors and Affiliations

  1. Faculty of Engineering, China University of Geosciences, Wuhan, 430074, China

    Qiong Wu, Yanjun Xu, Huiming Tang, Kun Fang, Yaofei Jiang, Chaoyuan Liu & Xiaohan Wang

  2. Department of Urban Management, Kyoto University, Nishikyo-ku, Kyoto, 615-8540, Japan

    Kun Fang

Authors
  1. Qiong Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yanjun Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Huiming Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kun Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yaofei Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chaoyuan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xiaohan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huiming Tang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest regarding the publication of this paper.

Appendix 1

Appendix 1

Table 9 The database used to train and validate the neural network
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, Q., Xu, Y., Tang, H. et al. Peak shear strength prediction for discontinuities between two different rock types using a neural network approach. Bull Eng Geol Environ 78, 2315–2329 (2019). https://doi.org/10.1007/s10064-018-1290-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10064-018-1290-x

Keywords

Search

Navigation