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

  • Qiong Wu
  • Yanjun Xu
  • Huiming TangEmail author
  • Kun Fang
  • Yaofei Jiang
  • Chaoyuan Liu
  • Xiaohan Wang
Original Paper


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.


Peak shear strength Discontinuities with different joint wall compressive strength (DDJCS) Discontinuities with identical joint wall compressive strength (DIJCS) Rock slope BP neural network Physical model test 



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.

Compliance with ethical standards

Conflict of interest

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


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of EngineeringChina University of GeosciencesWuhanChina
  2. 2.Department of Urban ManagementKyoto UniversityKyotoJapan

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