Abstract
The accurate determination of geomechanical properties such as uniaxial compressive strength and shear strength requires considerable time in collecting appropriate samples, their preparation and laboratory testing. To minimize the time and cost, a number of empirical relations have been reported which are widely used for the estimation of complex rock properties from more easily acquired data. This paper reports the use of an artificial neural network to predict the deformation properties of Coal Measure rocks using dynamic wave velocity, point load index, slake durability index and density. The results confirm the applicability of this method.
Résumé
La détermination précise des propriétés géomécaniques telles que la résistance à la compression simple et la résistance au cisaillement demande beaucoup de temps pour le choix des échantillons, leur préparation et la réalisation des essais de laboratoire. Afin de minimiser le temps et le coût, plusieurs relations empiriques ont été présentées, largement utilisées pour l’estimation des propriétés des roches à partir de données plus facilement obtenues. L’article présente l’utilisation d’un réseau de neurones artificiel destiné à prévoir les propriétés de déformation de roches d’une série houillère à partir de mesures de vitesses des ondes, de l’indice de compression entre pointes, l’indice de durabilité et la densité. Les résultats confirment l’applicabilité de cette méthode.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
GSI (1999) Shimla Quadrangle, 53E, Himachal Pradesh and Uttar Pradesh
Hubick K (1992) ANNs thinking for industry. Process Control Eng 15(11):36–38
Inoue M, Ohomi M (1981) Relation between uniaxial compressive strength and elastic wave velocity of soft rock, In: Akai K, Mayashi M, Nishimatsu Y (eds) Proc of the Int Symp on weak rock, Tokyo, pp 9–13
ISRM (1981) Rock characterization, testing and monitoring, ISRM suggested methods. Int Soc for Rock Mech, 211
Jaksa MB (1995) The influence of spatial variability on the geotechnical design properties of a stiff, over consolidated clay. PhD thesis, The University of Adelaide, Adelaide
Khandelwal M, Singh TN (2007) Evaluation of blast induced ground vibration predictors. Soil Dyn Earthq Eng 27(2):116–125
Kosko B (1994) Neural networks, fuzzy system; a dynamical system approach to machine intelligence. Prentice–Hall of India, New Delhi, p 1217
Lee C, Sterling R (1992) Identifying probable failure modes for underground openings using a neural network. Int J Rock Mech Min Sci Geomech Abstr 29(1):4967
Lessard JS, Hadjigeorgiou J (1999) Modelling shear behavior of rough joints using neural networks, In: Vouille G, Berest P (eds) 20th century lessons, 21st century challenges. ISBN 9058090698:925–9
Meulenkamp F, Alvarez GN (1999) Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness. Int J Rock Mech Min Sci 36:29–39
Millar D, Clarici E (1994) Investigation of back-propagation artificial neural networks in modelling the stress-strain behaviour of sandstone rock. IEEE Int Conf Neural Netw 5:3326–3331
Millar DL, Hudson JA (1994) Performance monitoring of rock eng systems using neural networks. Trans Inst Min Metall Sec A 103:3–16
Narula PL, Shanker R, Chopra C (2000) Rupture mechanism of Chamoli earthquake of 29th March 1999 and its implications for seismotectonics of Garwal Himalaya. J Geol Soc India 55(5):493–503
Sarkar K, Singh TN, Reddy DV (2009) Prediction of strength parameters by dynamic wave. Int J Earth Sci Eng 2(1):12–19
Simpson PK (1990) Artificial neural system-foundation, paradigm application and implementations. Pergamon Press, New York
Singh TN, Dubey RK (2000) A study of transmission velocity of primary wave (P-Wave) in Coal Measures sandstone. J Sci Ind Res India 59:482–486
Singh VK, Singh D, Singh TN (2001) Prediction of strength properties of some schistose rocks. Int Rock Mech Min Sci 38(2):269–284
Singh TN, Kanchan R, Saigal K, Verma AK (2004) Prediction of P-wave velocity and anisotropic properties of rock using Artificial Neural Networks technique. J Sci Ind Res 63(1):32–38
Singh TN, Kanchan R, Verma AK, Saigal K (2005) A comparative study of ANN and neuro-fuzzy for the prediction of dynamic constant of rockmass. J Earth Syst Sci 114(1):75–86
Sirat M, Talbot CJ (2001) Application of artificial neural networks to fracture analysis at the A. spo. HRL, Sweden: fracture sets classification. Int J Rock Mech Min Sci 38(5):62139
Tiryaki B (2008) Predicting intact rock strength for mechanical excavation using multivariate statistics, artificial neural networks, and regression trees. Eng Geol 99:51–60
Yilmaz I, Yuksek AG (2008) Technical note: an example of artificial neural network (ANN) application for indirect estimation of rock parameters. Rock Mech Rock Eng 41(5):781–795
Acknowledgments
One of the authors is grateful to CSIR, New Delhi, for providing financial assistance to complete the work.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Sarkar, K., Tiwary, A. & Singh, T.N. Estimation of strength parameters of rock using artificial neural networks. Bull Eng Geol Environ 69, 599–606 (2010). https://doi.org/10.1007/s10064-010-0301-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10064-010-0301-3