The performance of ANFIS model for prediction of deformation modulus of rock mass

The purpose of this study is to investigate the performance of adaptive neuro-fuzzy inference system (ANFIS) model in the estimation of the deformation modulus of rock mass. ANFIS is a powerful processing tool which is used for the modeling of complex problems where the relationship between the model variables is unknown. For this reason, this model seems to be suited for the estimation of deformation modulus. In this paper, the ANFIS model was constructed and compared with empirical relation that was suggested for indirect estimation of this parameter. In the ANFIS model, five parameters, including depth, uniaxial compressive strength of intact rock, RQD, spacing of discontinuities, and the condition of discontinuities are considered. These parameters are the most effective parameters in the estimation of deformation modulus. Employing the ANFIS model for the estimation of rock mass deformation modulus shows a reliable performance. The values of correlation coefficient, variance accounted for, and root mean square error of the results for ANFIS model is obtained as 0.86, 85.3%, and 2.73, respectively, which indicates precise and correlate results.

[1]  Biswajeet Pradhan,et al.  Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia , 2011, Expert Syst. Appl..

[2]  Morteza Beiki,et al.  Estimation of deformation modulus of rock masses by using fuzzy clustering-based modeling , 2011 .

[3]  C. Kayadelen,et al.  Adaptive neuro-fuzzy modeling for the swelling potential of compacted soils , 2009 .

[4]  T. N. Singh,et al.  Estimation of elastic constant of rocks using an ANFIS approach , 2012, Appl. Soft Comput..

[5]  E. Hoek,et al.  Empirical estimation of rock mass modulus , 2006 .

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

[7]  Z. Bieniawski,et al.  A nonlinear deformation modulus based on rock mass classification , 1990 .

[8]  I A Basheer,et al.  Artificial neural networks: fundamentals, computing, design, and application. , 2000, Journal of microbiological methods.

[9]  Manoj Khandelwal,et al.  Predicting elastic properties of schistose rocks from unconfined strength using intelligent approach , 2011 .

[10]  Nick Barton,et al.  Some new Q-value correlations to assist in site characterisation and tunnel design , 2002 .

[11]  Michio Sugeno,et al.  An introductory survey of fuzzy control , 1985, Inf. Sci..

[12]  Z. Bieniawski.  Determining rock mass deformability: experience from case histories , 1978 .

[13]  A. E. Tercan,et al.  Spatial estimation of some mechanical properties of rocks by fuzzy modelling , 2007 .

[14]  Byung-Sik Chun,et al.  Indirect estimation of the rock deformation modulus based on polynomial and multiple regression analyses of the RMR system , 2009 .

[15]  M. Monjezi,et al.  Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach , 2012, Arabian Journal of Geosciences.

[16]  Manfred F. Buchroithner,et al.  Landslide Susceptibility Mapping by Neuro-Fuzzy Approach in a Landslide-Prone Area (Cameron Highlands, Malaysia) , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[17]  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 .

[18]  Herbert H. Einstein,et al.  Using RQD to estimate the deformation modulus of rock masses , 2004 .

[19]  Biswajeet Pradhan,et al.  A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS , 2013, Comput. Geosci..

[20]  Biswajeet Pradhan,et al.  Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS , 2012, Comput. Geosci..

[21]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[22]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[23]  Li Wu,et al.  Fuzzy inference systems-based approaches in geotechnical engineering- a review , 2011 .

[24]  Masoud Monjezi,et al.  Prediction and controlling of flyrock in blasting operation using artificial neural network , 2011 .

[25]  Ali Firat Cabalar,et al.  Some applications of Adaptive Neuro-Fuzzy Inference System (ANFIS) in geotechnical engineering , 2012 .

[26]  C. Gokceoğlu,et al.  Estimating the deformation modulus of rock masses: a comparative study , 2003 .

[27]  Candan Gokceoglu,et al.  Technical Note Indirect determination of the modulus of deformation of rock masses based on the GSI system , 2004 .

[28]  Biswajeet Pradhan,et al.  Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area , 2011, Comput. Geosci..

[29]  Candan Gokceoglu,et al.  A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock , 2004, Eng. Appl. Artif. Intell..

[30]  Arild Palmström,et al.  The deformation modulus of rock masses — comparisons between in situ tests and indirect estimates , 2001 .

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

[32]  Chang-fu Chen,et al.  - 93-Stability Assessment Model for Epimetamorphic Rock Slopes based on Adaptive Neuro-Fuzzy Inference System , 2011 .

[33]  Candan Gokceoglu,et al.  A neuro-fuzzy model for modulus of deformation of jointed rock masses , 2004 .

[34]  Pejman Tahmasebi,et al.  Application of Adaptive Neuro-Fuzzy Inference System for Grade Estimation; Case Study, Sarcheshmeh Porphyry Copper Deposit, Kerman, Iran , 2010 .

[35]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[36]  Evert Hoek,et al.  Practical estimates of rock mass strength , 1997 .

[37]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..