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

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.

[1]  Louis Ngai Yuen Wong,et al.  Choosing Appropriate Parameters for Developing Empirical Shear Strength Criterion of Rock Joint: Review and New Insights , 2016, Rock Mechanics and Rock Engineering.

[2]  C. Gokceoğlu,et al.  Predicting the deformation moduli of rock masses , 2003 .

[3]  Jaime García-Pérez,et al.  Optimum Seismic Zoning Using An Artificial Neural Network , 2016 .

[4]  C. Hsein Juang,et al.  A neural network approach to estimating deflection of diaphragm walls caused by excavation in clays , 2007 .

[5]  Giovanni Grasselli,et al.  Constitutive law for the shear strength of rock joints based on three-dimensional surface parameters , 2003 .

[6]  Amin Barari,et al.  Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran) , 2009 .

[7]  Holger R. Maier,et al.  The effect of internal parameters and geometry on the performance of back-propagation neural networks: an empirical study , 1998 .

[8]  F. Patton.  Multiple Modes of Shear Failure In Rock , 1966 .

[9]  Fang Ku.  Research on Shear Characteristics and the Evolution Mechanism of Bedding Planes Between Two Different Rock Types Based on Particle Flow Code , 2014 .

[10]  Aminaton Marto,et al.  Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm , 2015, Bulletin of Engineering Geology and the Environment.

[11]  Satar Mahdevari,et al.  Prediction of tunnel convergence using Artificial Neural Networks , 2012 .

[12]  Masoud Monjezi,et al.  Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm , 2016, Bulletin of Engineering Geology and the Environment.

[13]  T. N. Singh,et al.  Indirect estimation of compressive and shear strength from simple index tests , 2016, Engineering with Computers.

[14]  S. R. Hencher,et al.  Assessing the Shear Strength of Rock Discontinuities at Laboratory and Field Scales , 2015, Rock Mechanics and Rock Engineering.

[15]  T. G. Sitharam,et al.  Prediction of Elastic Modulus of Jointed Rock Mass Using Artificial Neural Networks , 2008 .

[16]  Abbas Majdi,et al.  Evolving neural network using a genetic algorithm for predicting the deformation modulus of rock masses , 2010 .

[17]  G. Lewicki,et al.  Approximation by Superpositions of a Sigmoidal Function , 2003 .

[18]  Hyun-Sic Jang,et al.  New Method for Shear Strength Determination of Unfilled, Unweathered Rock Joint , 2015, Rock Mechanics and Rock Engineering.

[19]  Abbas Majdi,et al.  Genetic programming approach for estimating the deformation modulus of rock mass using sensitivity analysis by neural network , 2010 .

[20]  S. R. Hencher,et al.  A Comprehensive Peak Shear Strength Criterion For Rock Joints , 1995 .

[21]  D. V. Griffiths,et al.  Neural network prediction of the reliability of heterogeneous cohesive slopes , 2016 .

[22]  Nick Barton,et al.  Review of a new shear-strength criterion for rock joints , 1973 .

[23]  Anthony T. C. Goh,et al.  Estimating wall deflections in deep excavations using Bayesian neural networks , 2005 .

[24]  Yanwen Wu,et al.  Software Engineering and Knowledge Engineering: Theory and Practice , 2012 .

[25]  N. Draper,et al.  Applied Regression Analysis. , 1967 .

[26]  Gali Madhavi Latha,et al.  Intelligent prediction of the stress-strain response of intact and jointed rocks , 2010 .

[27]  Wu Qiong,et al.  Mean particle size prediction in rock blast fragmentation using neural networks , 2010 .

[28]  Ali Mirzaghorbanali,et al.  Effects of Cyclic Loading on the Shear Behaviour of Infilled Rock Joints Under Constant Normal Stiffness Conditions , 2014, Rock Mechanics and Rock Engineering.

[29]  Gérard Ballivy,et al.  Comparison of Joint Shearing Resistance Obtained with the Barton and Choubey Criterion and with Direct Shear Tests , 2016, Rock Mechanics and Rock Engineering.

[30]  David Haussler,et al.  What Size Net Gives Valid Generalization? , 1989, Neural Computation.

[31]  A. Ghazvinian,et al.  The Shear Behavior of Bedding Planes of Weakness Between Two Different Rock Types with High Strength Difference , 2010 .

[32]  Christos Polykretis,et al.  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) , 2015, Bulletin of Engineering Geology and the Environment.

[33]  Huiming Tang,et al.  Application of back-propagation neural network on bank destruction forecasting for accumulative landslides in the three Gorges Reservoir Region, China , 2014, Stochastic Environmental Research and Risk Assessment.

[34]  Hao Wang,et al.  Predicting tunnel convergence using Multivariate Adaptive Regression Spline and Artificial Neural Network , 2013 .

[35]  Mahdi Moosavi,et al.  The Influence of Shearing Velocity on Shear Behavior of Artificial Joints , 2014, Rock Mechanics and Rock Engineering.

[36]  Giuseppe Modoni,et al.  Prediction of the diameter of jet grouting columns with artificial neural networks , 2015 .

[37]  Ahmed M. Azmy,et al.  Neural networks for predicting compressive strength of structural light weight concrete , 2009 .

[38]  Timothy Masters,et al.  Practical neural network recipes in C , 1993 .

[39]  Pinnaduwa Kulatilake,et al.  New peak shear strength criteria for anisotropic rock joints , 1995 .

[40]  Candan Gokceoglu,et al.  Estimation of rock modulus: For intact rocks with an artificial neural network and for rock masses with a new empirical equation , 2006 .

[41]  L. L. Rogers,et al.  Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling , 1994 .

[42]  M. Maksimović,et al.  New description of the shear strength for rock joints , 1992 .

[43]  Louis Ngai Yuen Wong,et al.  Influences of Normal Loading Rate and Shear Velocity on the Shear Behavior of Artificial Rock Joints , 2016, Rock Mechanics and Rock Engineering.

[44]  Ali Firat Cabalar,et al.  Modeling deformation modulus of a stratified sedimentary rock mass using neural network, fuzzy inference and genetic programming , 2016 .

[45]  Elizabeth A. Peck,et al.  Introduction to Linear Regression Analysis , 2001 .

[46]  Zhigang Zeng,et al.  Multiple neural networks switched prediction for landslide displacement , 2015 .

[47]  B. S. Lim,et al.  Optimal design of neural networks using the Taguchi method , 1995, Neurocomputing.