Predicting rock mass deformation modulus by artificial intelligence approach based on dilatometer tests

Barton N (1978) Suggested methods for the quantitative description of discontinuities in rock masses: International Society for Rock Mechanics. Int J Rock Mech Min Sci Geomech Abstr 15(6):319–368

Barton N (2002) some new Q-value correlations to assist in site characterization and tunnel design. Int J Rock Mech Min Sci 39:185–216

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

Bieniawski ZT (1974) Geomechanics classification of rock masses and its application in tunneling. In: Proceedings of the third congress of the international society for rock mechanics. Denver: pp 23–32

Bieniawski ZT (1979) The geomechanics classification in rockengineering applications. In: Proceedings of the fourth congress of ISRM, vol 2, Montreux, pp 41–48

Bishop C.M. (1995) Neural Networks for Pattern Recognition. Oxford University Press, New York, pages 482: 165–171

BJVC (2009a) Bakhtiary Dam and HEPP, Engineering geology and rock mechanics report; Report No 4673/4049 Rev 1

BJVC (2009b) Bakhtiary Dam and HEPP, Geological Report, Report No. 4673/4038 Rev 1

Boyd RD (1993) Elastic properties of jointed rock masses with regard to their rock mass rating value. In: Cripps JC et al (eds) The engineering geology of weak rock. Balkema, Rotterdam, pp 329–336

Broomhead DS, Lowe D (1988) Multivariable functional interpolation and adaptive networks. Complex Systems 2:321–355

Cai M, Kaiser PK, Uno H, Tasaka Y, Minami M (2004) Estimation of rock mass deformation modulus and strength of jointed hard rock masses using the GSI system. Int J Rock Mech Min Sci 41:3–19

Ding L, Zhou C (2013) Development of web-based system for safety risk early warning in urban metro construction. Automat Constr 34:45–55

Fahimifar A, Soroush H (2003) Rock mechanics tests, theoretical aspects and standards. Vol. No 1, Published by soil mechanics Laboratory, Tehran

Franklin JA, Dusseault MB (1989) Rock engineering. McGraw-Hill, New York. José M

Ghasemi E, Amini H, Ataei M, Khalokakaei R (2014) Application of artificial intelligenc techniques for predicting the flyrock distance caused by blasting operation. Arab J Geosci 7:193–202

Goodman, Richard E. (1989) Introduction to rock mechanics. Second edition, John Wiley& Sons, New York

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

Grima MA (2000) Neuro-fuzzy modelling in engineering geology. A.A. Balkema, Rotterdam

Guo H, Lin S, Liu J (2006) A radial basis function sliding mode controller for chaotic Lorenz system. Physics Letters A 351:257–261

Habibagahi G, Katebi S (1996) Rockmass classification using fuzzy sets. Iran J Sci Technol Trans B 20(3):273–284

Den Hartog MH, Babuska R, Deketh HJR, Grima MA, Verhoef PNW, Verbruggen HB (1997) Knowledge-based fuzzy model for performance prediction of a rock-cutting trencher. Int J Approx Reason 16(1):43–66

Ham F., Kostanic I. (2001) Principles of Neurocomputing for Science & Engineering. McGraw-Hill, New York

Hoek K (1983) Strength of jointed rock masses. Géotechnique J 23(3):187–223

Hoek E (1994) Strength of rock and rock masses. ISRM New J 2(2):4–16

Hoek E, Brown ET (1997) Practical estimates of rock mass strength. Int J Rock Mech Min Sci 40:701–710

Hoek E, Carranza-Torres C, Corkum B (2002) Hoek–Brown failure criterion-2002 edition. In: Proceedings of 5th North American Rock Mechanics Symposium and Tunneling Association of Canada Conference: NARMS-TAC pp 267–271

Hoek E, Kaiser PK, Bawden WF (1995) Support of underground excavations in hard rock. Balkema, Rotterdam, p 215

Hoek E, Diederichs MS (2006) Empirical estimation of rock mass modulus. Int J Rock Mech Min Sci 43:203–215

Interfels (2002) Dilatometer system, Boart Longyear interfels Gmbh, available at http://www.interfels.com

Jang RJS, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing. Prentice-Hall, Upper Saddle River

Karami A, Afiuni-Zadeh S (2012) Sizing of rock fragmentation modeling due to bench blasting using adaptive neuro-fuzzy inference system and radial basis function. Int J Min Sci Technol 22:459–463

Kayabasi A, Gokceoglu C, Ercanoglu M (2003) Estimating the deformation modulus of rock masses: a comparative study. Int J Rock Mech Min Sci 40:55–63

Ladanyi B (1987) Suggested methods for deformability determination using a flexible dilatometer. Int J Rock Mech Min Sci 24(2):123–134

Luo FL, Unbehauen R (1999) Applied neural networks for signal processing. Cambridge University Press, Cambridge

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 47:246–53

Mitri HS, Edrissi R, Henning J (1994) Finite element modeling of cable-bolted slopes in hard rock ground mines. Presented at the SME annual meeting. Albuquerque, New Mexico, In, pp 94–116

Moosavi M, Yazdanpanah MJ, Doostmohammadi R (2006) Modelling the cyclic swelling pressure of mudrock using artificial neural networks. Eng Geol 87:178–94

Motiei H (1993) Geology of Iran, stratigraphy of Zagros. Geological Survey of Iran publication—Tehran, Iran, In Persian

Narendra BS, Sivapullaiah PV, Suresh SN, Omkar SN (2006) Prediction of unconfined compressive strength of soft grounds using computational intelligence techniques: a comparative study. Comput Geotech 33:196–208

Palmström A, Singh R (2001) The deformation modulus of rock masses—comparisons between in situ tests and indirect esti-mates. Tunnel Undergr Space Technol 16:115–131

Rojas R (1996) Neural networks: a systematic introduction. Springer Verlag, Berlin

Ross T (1995) Fuzzy logic with engineering applications. McGraw-Hill Inc., New York

Serafim JL, Pereira JP (1983) Considerations on the geomechanical classification of Beniawski: experience from case histories. Proceedings of symposium on engineering geology and under-ground openings, Lisbon, In, pp 1133–1144

Sridevi J, Sitharam TG (2003) Characterization of strength and deformation of jointed Rock Mass based on statistical analysis. Int Journal Geomech 3.1:43–54

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 36:743–760

Sonmez H, Gokceoglu C, Ulusay R (2004) indirect determination of the modulus of deformation of rock masses based on GSI system. Int J Rock Mech Min Sci 41:849–857 equation. Int J Rock Mech Min Sci 43:224–235

Tan Y, Zhang Z (2011) A RBF neural network approach for fitting creep curve of sandstone. Adv Mater Res Vols 171–172:274–277

Verman M, Singh B, Viladkar MN, Jethwa JL (1997) Effect of tunnel depth on modulus of deformation of rock mass. Rock Mech Rock Eng 30(3):121–127

Yilmaz I, Kaynar O (2011) Multiple Regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Syst Appl 38:5958–5966

Yow JL Jr (1993) Borehole dilatometer testing for rock engineering. Comprehensive rock engineering principles, practice and projects, Vol. 3. Pergamon Press, Oxford, pp 671–682

Zhang L, Einstein HH (2004) Using RQD to estimate the deformation modulus of rock masses. Int J Rock Mech Min Sci 41:337–341

Zhouquan L, Hongyan Z, Nan J, Yiwei W (2013) Instability identification on large scale underground mined-out area in the metal mine based on the improved FRBFNN. Int J Min Sci Technol 23:821–826