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http://dx.doi.org/10.12989/gae.2021.27.5.511

Predicting the shear strength parameters of rock: A comprehensive intelligent approach  

Fattahi, Hadi (Faculty of Earth Sciences Engineering, Arak University of Technology)
Hasanipanah, Mahdi (Institute of Research and Development, Duy Tan University)
Publication Information
Geomechanics and Engineering / v.27, no.5, 2021 , pp. 511-525 More about this Journal
Abstract
In the design of underground excavation, the shear strength (SS) is a key characteristic. It describes the way the rock material resists the shear stress-induced deformations. In general, the measurement of the parameters related to rock shear strength is done through laboratory experiments, which are costly, damaging, and time-consuming. Add to this the difficulty of preparing core samples of acceptable quality, particularly in case of highly weathered and fractured rock. This study applies rock index test to the indirect measurement of the SS parameters of shale. For this aim, two efficient artificial intelligence methods, namely (1) adaptive neuro-fuzzy inference system (ANFIS) implemented by subtractive clustering method (SCM) and (2) support vector regression (SVR) optimized by Harmony Search (HS) algorithm, are proposed. Note that, it is the first work that predicts the SS parameters of shale through ANFIS-SCM and SVR-HS hybrid models. In modeling processes of ANFIS-SCM and SVR-HS, the results obtained from the rock index tests were set as inputs, while the SS parameters were set as outputs. By reviewing the obtained results, it was found that both ANFIS-SCM and SVR-HS models can provide acceptable predictions for interlocking and friction angle parameters, however, ANFIS-SCM showed a better generalization capability.
Keywords
ANFIS; hybrid models; shear strength; SVR;
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1 Khandelwal, M., Marto, A., Fatemi, S.A., Ghoroqi, M., Armaghani, D.J., Singh, T.N. and Tabrizi, O. (2018), "Implementing an ANN model optimized by genetic algorithm for estimating cohesion of limestone samples", Eng. Comput., 34(2), 307-317. https://doi.org/10.1007/s00366-017-0541-y.   DOI
2 Le, T.T., Asteris, P.G. and Lemonis, M.E. (2021), "Prediction of axial load capacity of rectangular concrete-filled steel tube columns using machine learning techniques", Eng. Comput. https://doi.org/10.1007/s00366-021-01461-0.   DOI
3 Liu, L., Yang, C. and Wang, X. (2021), "Landslide susceptibility assessment using feature selection-based machine learning models", Geomech. Eng., 25(1), 1-16. http://dx.doi.org/10.12989/gae.2021.25.1.001.   DOI
4 Jahed Armaghani, D., Hajihassani, M., Yazdani Bejarbaneh, B., Marto, A. and Tonnizam Mohamad, E. (2014), "Indirect Measure of Shale Shear Strength Parameters by Means of Rock Index Tests through an Optimized Artificial Neural Network", Measurement, 55, 487-498. https://doi.org/10.1016/j.measurement.2014.06.001.   DOI
5 Eberhart, R.C. and Shi, Y. (1998), "Evolving artificial neural networks", Proceedings of the International Conference on Neural Networks and Brain, PL5-PL13, Beijing, October.
6 Alejano, L.R. and Carranza-Torres, C. (2011), "An empirical approach for estimating shear strength of decomposed granites in Galicia, Spain", Eng. Geol., 120(1-4), 91-102. https://doi.org/10.1016/j.enggeo.2011.04.003   DOI
7 Amann, F., Kaiser, P. and Button, E.A. (2012), "Experimental study of brittle behavior of clay shale in rapid triaxial compression", Rock Mech. Rock Eng., 45, 21-33. https://doi.org/10.1007/s00603-011-0195-9   DOI
8 Kainthola, A., Singh, P.K., Verma, D., Singh, R., Sarkar, K. and Singh, T.N. (2015), "Prediction of strength parameters of himalayan rocks: a statistical and ANFIS approach", Geotech. Geol. Eng., 33, 1255-1278. https://doi.org/10.1007/s10706-015-9899-z.   DOI
9 Bouayad, D. and Emeriault, F. (2017), "Modeling the relationship between ground surface settlements induced by shield tunneling and the operational and geological parameters based on the hybrid PCA/ANFIS method", Tunn. Undergr. Sp. Technol., 80, 1-9. https://doi.org/10.1016/j.tust.2017.03.011.   DOI
10 Chiu, S.L. (1994), "Fuzzy model identification based on cluster estimation", J. Intell. Fuzzy Syst., 2, 267-278. https://doi.org/10.3233/IFS-1994-2306.   DOI
11 Murlidhar, B.R., Ahmed M., Mavaluru D., Siddiqi A.F. and Mohamad E.T. (2018), "Prediction of rock interlocking by developing two hybrid models based on GA and fuzzy system", Eng. Comput., 35, 1419-1430. https://doi.org/10.1007/s00366-018-0672-9.   DOI
12 Apostolopoulou, M., Asteris, P.G., Armaghani, D.J., Douvika, M.G., Lourenco, P.B., Cavaleri, L., Bakolas, A. and Moropoulou, A. (2020), "Mapping and holistic design of natural hydraulic lime mortars", Cem. Concr. Res., 136, 106167, https://doi.org/10.1016/j.cemconres.2020.106167.   DOI
13 Asadi, M. and Bagheripour, M.H. (2013), "Numerical and intelligent modeling of triaxial strength of anisotropic jointed rock specimens", Earth Sci. Inform., 7, 165-172. https://doi.org/10.1007/s12145-013-0137-z.   DOI
14 Mclamore, R. and Gray, K. (1990), The Mechanical Behaviour of Anisotropic Sedimentary Rocks, J. Eng. Ind., 89(1), 62-73. https://doi.org/10.1115/1.3610013.   DOI
15 Moayedi, H. and Armaghani, D.J. (2018), "Optimizing an ANN model with ICA for estimating bearing capacity of driven pile in cohesionless soil", Eng. Comput., 34(2), 347-356. https://doi.org/10.1007/s00366-017-0545-7.   DOI
16 Monjezi, M., Hasanipanah, M. and Khandewal, M. (2013), "Evaluation and prediction of blast-induced ground vibration at Shur River Dam Iran, by artificial neural network", Neural Comput. Appl., 22, 1637-1643. https://doi.org/10.1007/s00521-012-0856-y.   DOI
17 Fattahi, H. (2016), "Application of improved support vector regression model for prediction of deformation modulus of a rock mass", Eng. Comput., 32, 567-580. https://doi.org/10.1007/s00366-016-0433-6.   DOI
18 Fattahi, H. (2020), "A New Method for Forecasting of Uniaxial Compressive Strength of Weak Rocks", J. Mining Environ., 11(2), 505-515. https://doi.org/10.22044/jme.2020.9328.1835.   DOI
19 Hasanipanah, M. and Bakhshandeh Amnieh, H. (2020b), "Developing a new uncertain rule-based fuzzy approach for evaluating the blast-induced backbreak", Eng. Comput., 37(3), 1879-1893. https://doi.org/10.1007/s00366-019-00919-6.   DOI
20 Mottahedi, A., Sereshki, F. and Ataei, M. (2018), "Overbreak prediction in underground excavations using hybrid ANFIS-PSO model", Tunn. Undergr. Sp. Technol., 68, 142-152. https://doi.org/10.1016/j.tust.2018.05.023.   DOI
21 Sadeghi, F., Monjezi, M. and Armaghani, D.J. (2020), "Evaluation and optimization of prediction of toe that arises from mine blasting operation using various soft computing techniques", Nat. Resour. Res., 29(2), 887-903. https://doi.org/10.1007/s11053-019-09605-2.   DOI
22 Hasanipanah, M., Meng, D., Keshtegar, B., Trung, N.T. and Thai, D.K. (2020b), "Nonlinear models based on enhanced Kriging interpolation for prediction of rock joint shear strength", Neural Comput. Appl., 33(9), 4205-4215. https://doi.org/10.1007/s00521-020-05252-4.   DOI
23 Mishra, D.A., Srigyan, M., Basu, A. and Rokade, P.J. (2015), "Soft computing methods for estimating the uniaxial compressive strength of intact rock from index tests", Int. J. Rock Mech. Min. Sci., 80, 418-424. https://doi.org/10.1016/j.ijrmms.2015.10.012.   DOI
24 Fattahi, H. and Babanouri, N. (2017), "Predicting tensile strength of rocks from physical properties based on support vector regression optimized by cultural algorithm", J. Mining Environ., 8(3), 467-474. https://doi.org/10.22044/jme.2016.824.   DOI
25 Geem, Z.W. (2009), Music-inspired Harmony Search Algorithm: Theory and Applications, Springer Verlag, Berlin, Germany.
26 Geem, Z.W., Kim, J.H. and Loganathan, G.V. (2001), "A new heuristic optimization algorithm: harmony search simulation", Simulation, 76(2), 60-68. https://doi.org/10.1177/003754970107600201.   DOI
27 Hajdarwish, A. and Shakoor, A. (2006), "Predicting the shear strength parameters of mudrocks", Geol. Soc. London, 2, 607.
28 Ghazvinian, A. and Hadei, M.R. (2012), "Effect of discontinuity orientation and confinement on the strength of jointed anisotropic rocks", Int. J. Rock Mech. Min. Sci., 55, 117-124. http://dx.doi.org/10.1016/j.ijrmms.2012.06.008.   DOI
29 Hasanipanah, M. and Bakhshandeh Amnieh, H. (2020a), "A fuzzy rule based approach to address uncertainty in risk assessment and prediction of blast-induced flyrock in a quarry", Nat. Resour. Res., 29(2), 669-689. https://doi.org/10.1007/s11053-020-09616-4.   DOI
30 Harandizadeh, H., Armaghani, D.J., Asteris, P.G. and Gandomi, A.H. (2021), "TBM performance prediction developing a hybrid ANFIS-PNN predictive model optimized by imperialism competitive algorithm", Neural Comput. Appl., 33(23), 16149-16179. https://doi.org/10.1007/s00521-021-06217-x.   DOI
31 Hasanipanah, M., Noorian-Bidgoli, M., Jahed Armaghani, D. and Khamesi, H. (2016), "Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling", Eng. Comput., 32, 705-715. https://doi.org/10.1007/s00366-016-0447-0.   DOI
32 Hasanipanah, M., Zhang, W., Armaghani, D.J. and Rad, H.N. (2020c), "The potential application of a new intelligent based approach in predicting the tensile strength of rock", IEEE Access, 8, 57148-57157. https://doi.org/10.1109/ACCESS.2020.2980623.   DOI
33 Hong, W.C. (2011), "Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm", Energy, 36, 5568-5578. https://doi.org/10.1016/j.energy.2011.07.015.   DOI
34 Huang, J., Duan, T., Zhang, Y., Liu, J., Zhang, J. and Lei, Y. (2020), "Predicting the permeability of pervious concrete based on the beetle antennae search algorithm and random forest model", Adv. Civ. Eng., 2, 8863181. https://doi.org/10.1155/2020/8863181.   DOI
35 Singh, M. and Singh, B. (2012), "Modified Mohr-Coulomb criterion for non-linear triaxial and polyaxial strength of jointed rocks", Int. J. Rock Mech. Min. Sci., 51, 43-52. https://doi.org/10.1016/j.ijrmms.2011.12.007.   DOI
36 Iannacchione, A.T. and Vallejo, L.E. (2000), "Shear strength evaluation of clay-rock mixtures", Slope Stability 2000, ASCE Geotechnical Special Publication 101, 209-223, Denver, August.
37 International Society for Rock Mechanics (ISRM). (2007), "The complete ISRM suggested methods for rock characterization, testing and monitoring: 1974-2006". Ulusay R, Hudson JA (eds) Suggested methods prepared by the commission on testing methods, International Society for Rock Mechanics, ISRM Turkish National Group, Ankara, Turkey,\
38 Iphar, M. (2012), "ANN and ANFIS performance prediction models for hydraulic impact hammers", Tunn. Undergr. Sp. Technol., 27, 23-29. https://doi.org/10.1016/j.tust.2011.06.004.   DOI
39 Asteris, P.G., Mamou, A., Hajihassani, M., Hasanipanah, M., Koopialipoor, M., Le, T.T., Kardani, N. and Jahed Armaghani, D. (2021), "Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks", Transp. Geotech., 29, 100588. https://doi.org/10.1016/j.trgeo.2021.100588.   DOI
40 Asteris, P.G., Argyropoulos, I., Cavaleri, L., Rodrigues, H., Varum, H., Thomas, J. and Lourenco, P.G. (2018), "Masonry compressive strength prediction using artificial neural networks", International Conference on Transdisciplinary Multispectral Modeling and Cooperation for the Preservation of Cultural Heritage, Athens, October.
41 Yang, H., Nikafshan Rad, H., Hasanipanah, M., Bakhshandeh Amnieh, H. and Nekouie, A. (2020), "Prediction of Vibration Velocity Generated in Mine Blasting Using Support Vector Regression Improved by Optimization Algorithms", Nat. Resour. Res., 29, 807-830. https://doi.org/10.1007/s11053-019-09597-z.   DOI
42 Singh, R., Kainthola, A. and Singh, T.N. (2012), "Estimation of elastic constant of rocks using an ANFIS approach", Appl. Soft Comput., 12(1), 40-45. https://doi.org/10.1016/j.asoc.2011.09.010.   DOI
43 Taghavifar, H. and Mardani, A. (2014), "Prognostication of vertical stress transmission in soil profile by adaptive neuro-fuzzy inference system based modeling approach", Measurement, 50, 152-159. https://doi.org/10.1016/j.measurement.2013.12.035.   DOI
44 Teymen, A. and Menguc, E.C. (2020), "Comparative evaluation of different statistical tools for the prediction of uniaxial compressive strength of rocks", Int. J. Min. Sci. Technol., 30(6), 785-797. https://doi.org/10.1016/j.ijmst.2020.06.008.   DOI
45 Yang, Y. and Zang, O. (1997), "A hierarchical analysis for rock engineering using artificial neural networks", Rock Mech. Rock Eng., 30, 207-222. https://doi.org/10.1007/BF01045717.   DOI
46 Singh, T.N., Kanchan, R., Verma, A.K. and Saigal, K. (2005), "A comparative study of ANN and Neuro-fuzzy for the prediction of dynamic constant of rockmass", J. Earth Syst. Sci., 114, 75-86. https://doi.org/10.1007/BF02702010.   DOI
47 Adhikari, R. and Agrawal, R.K. (2011), "Effectiveness of PSO based neural network for seasonal time series forecasting", Proceedings of the Indian International Conference on Artificial Intelligence (IICAI), Tumkur, December.
48 Armaghani, D.J., Mirzaei, F., Toghroli, A. and Shariati, A. (2020a), "Indirect measure of shear strength parameters of fiber-reinforced sandy soil using laboratory tests and intelligent systems", Geomech. Eng., 22(5), 397-414. http://dx.doi.org/10.12989/gae.2020.22.5.397.   DOI
49 Asteris, P.G. and Mokos, V.G. (2019), "Concrete compressive strength using artificial neural networks", Neural Comput. Appl. https://doi.org/10.1007/s00521-019-04663-2.   DOI
50 Sharma, L.K., Vishal, V. and Singh, T.N. (2017), "Developing novel models using neural networks and fuzzy systems for the prediction of strength of rocks from key geomechanical properties", Measurement, 102, 158-169. https://doi.org/10.1016/j.measurement.2017.01.043.   DOI
51 Wu, J.D., Hsu, C.C. and Wu, G.Z. (2009), "Fault gear identification and classification using discrete wavelet transform and adaptive neuro-fuzzy inference", Expert Syst. Appl., 36, 6244-6255. https://doi.org/10.1016/j.eswa.2008.07.023.   DOI
52 Yazdani, B. (2012), "Shear Strength Parameters of Shale Based on Triaxial Compression Test", M.Sc. Dissertation, Universiti Teknologi Malaysia.
53 Zhang, W., Li, H., Li, Y. Liu, H., Chen, Y. and Ding, X. (2021a), "Application of deep learning algorithms in geotechnical engineering: A short critical review", Artif. Intell. Rev., 1-41. https://doi.org/10.1007/s10462-021-09967-1   DOI
54 Islam, M.A. and Skalle, P. (2013), "An experimental investigation of shale mechanical properties through drained and undrained test mechanisms", Rock Mech. Rock Eng., 46, 1391-1413. https://doi.org/10.1007/s00603-013-0377-8.   DOI
55 Barton, N. (2013), "Shear strength criteria for rock, rock joints, rockfill and rock masses: problems and some solutions", J. Rock Mech. Geotech. Eng., 5(4), 249-261. https://doi.org/10.1016/j.jrmge.2013.05.008.   DOI
56 Chen, W., Hasanipanah, M., Nikafshan Rad, H., Jahed Armaghani, D. and Tahir, M.M. (2021), "A new design of evolutionary hybrid optimization of SVR model in predicting the blast-induced ground vibration", Eng. Comput., 37, 1455-1471. https://doi.org/10.1007/s00366-019-00895-x.   DOI
57 Hasanipanah, M., Keshtegar, B., Thai, D.K. and Troung, N.T. (2020a), "An ANN-adaptive dynamical harmony search algorithm to approximate the flyrock resulting from blasting", Eng. Comput., 2020, 1-13. https://doi.org/10.1007/s00366-020-01105-9.   DOI
58 Ye, J., Dalle, J., Nezami, R., Hasanipanah, M. and Armaghani, D.J. (2020), "Stochastic fractal search-tuned ANFIS model to predict blast-induced air overpressure", Eng. Comput., 1-15. https://doi.org/10.1007/s00366-020-01085-w.   DOI
59 Zhang, W. and Goh, A.T.C. (2013), "Multivariate adaptive regression splines for analysis of geotechnical engineering systems", Comput. Geotech., 48, 82-95. https://doi.org/10.1016/j.compgeo.2012.09.016.   DOI
60 Bai, X., Cheng, W., Ong, D.E.L. and Li, G. (2021), "Evaluation of geological conditions and clogging of tunneling using machine learning", Geomech. Eng., 25(1), 59-73. http://dx.doi.org/10.12989/gae.2021.25.1.059.   DOI
61 Brady, B.H. (2004), Rock Mechanics: For Underground Mining, Springer, Berlin, Germany.
62 Barla, G., Barla, M. and Debernardi, D. (2010), "New triaxial apparatus for rocks", Rock Mech. Rock Eng., 43, 225-230. https://doi.org/10.1007/s00603-009-0076-7.   DOI
63 Hoek, E., Carranza-Torres, C. and Corkum, B. (2002), "Hoek-Brown failure criterion-2002 edition", Proceedings of the 5th North American Rock Mechanics Symposium and 17th Tunnelling Association of Canada Conference: University of Toronto, Toronto, July.
64 Huang, J., Sun, Y. and Zhang, J. (2021), "Reduction of computational error by optimizing SVR kernel coefficients to simulate concrete compressive strength through the use of a human learning optimization algorithm", Eng. Comput. https://doi.org/10.1007/s00366-021-01305-x.   DOI
65 Jaeger, J.C., Cook, N.G.W. and Zimmerman, R. (2009), Fundamentals of Rock Mechanics, Wiley-Blackwell, NJ, USA.
66 Jahed Armaghani, D. and Asteris, P.G. (2021), "A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength", Neural Comput. Applic., 33, 4501-4532. https://doi.org/10.1007/s00521-020-05244-4.   DOI
67 Jahed Armaghani, D., Amin, M.F.M., Yagiz, S., Faradonbeh, R.S. and Abdullah, R.A. (2016), "Prediction of the uniaxial compressive strength of sandstone using various modeling techniques", Int. J. Rock Mech. Min. Sci., 85, 174-186. https://doi.org/10.1016/j.ijrmms.2016.03.018.   DOI
68 Zhou, J., Li, E., Wang, M., Chen, X., Shi, X. and Jiang, L. (2019), "Feasibility of stochastic gradient boosting approach for evaluating seismic liquefaction potential based on SPT and CPT case histories", J. Perform. Constr. Fac., 33(3), 04019024 . https://doi.org/10.1061/(ASCE)CF.1943-5509.0001292.   DOI
69 Asteris, P.G., Moropoulou, A., Skentou, A.D., Apostolopoulou, M., Mohebkhah, A., Cavaleri, L., Rodrigues, H. and Varum, H. (2019c), "Stochastic vulnerability assessment of masonry structures: Concepts, modeling and restoration Aspects", Appl. Sci., 9(2), 243. https://doi.org/10.3390/app9020243.   DOI
70 Bardhan, A., Gokceoglu, C., Burman, A., Samui, P. and Asteris, P.G. (2021), "Efficient computational techniques for predicting the California bearing ratio of soil in soaked conditions", Eng. Geol., 291, 106239. https://doi.org/10.1016/j.enggeo.2021.106239.   DOI
71 Bejarbaneh, B.Y., Bejarbaneh, E.Y., Amin, M.F.M., Fahimifar, A., Jahed Armaghani, D. and Majid, M.Z.A. (2018), "Intelligent modelling of sandstone deformation behaviour using fuzzy logic and neural network systems", Bull. Eng. Geol. Environ., 77, 345-361. https://doi.org/10.1007/s10064-016-0983-2.   DOI
72 Zhang, W., Zhang, R., Wu, C., Goh, A.T.C., Lacasse, S., Liu, Z. and Liu, H. (2020), "State-of-the-art review of soft computing applications in underground excavations", Geosci. Front., 11(4), 1095-1106. https://doi.org/10.1016/j.gsf.2019.12.003.   DOI
73 Jahed Armaghani, D., Safari, V., Fahimifar, A., Monjezi, M. and Mohammadi, M.A. (2018), "Uniaxial compressive strength prediction through a new technique based on gene expression programming", Neural Comput. Appl., 30(11), 3523-3532. https://doi.org/10.1007/s00521-017-2939-2.   DOI
74 Asteris, P.G., Apostolopoulou, M., Skentou, A.D. and Antonia Moropoulou, A. (2019a), "Application of artificial neural networks for the prediction of the compressive strength of cement-based mortars", Comput. Concr., 24(4), 329-345. http://dx.doi.org/10.12989/cac.2019.24.4.329.   DOI
75 Asteris, P.G., Armaghani, D.J., Hatzigeorgiou Karayannis, C.G. and Pilakoutas, K. (2019b), "Predicting the shear strength of reinforced concrete beams using artificial neural networks", Comput. Concr., 24(5), 469-488. http://dx.doi.org/10.12989/cac.2019.24.5.469.   DOI
76 Jang, J.S.R. (1993), "ANFIS adaptive-network-based fuzzy inference system", IEEE Trans. Syst. Man Cybern., 23, 665-685. https://doi.org/10.1109/21.256541.   DOI
77 Fattahi, H. (2017), "Applying soft computing methods to predict the uniaxial compressive strength of rocks from schmidt hammer rebound values", Computat. Geosci., 21, 665-681. https://doi.org/10.1007/s10596-017-9642-3.   DOI
78 Fattahi, H. and Bayat, N. (2019), "Forecasting of Rock Drillability Using a New Computational Intelligent Method", Geotech. Geol. Eng., 38, 5693. https://doi.org/10.1007/s10706-019-00971-5   DOI
79 Dantas Neto, S.A., Indraratna, B., Oliveira, D.A.F. and de Assis, A.P. (2017), "Modelling the shear behaviour of clean rock discontinuities using artificial neural networks", Rock Mech. Rock Eng., 50, 1817-1831. https://doi.org/10.1007/s00603-017-1197-z.   DOI
80 Asteris, P.G. and Nikoo, M. (2019), "Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures", Neural Comput. Appl., 31(9), 4837-4847. https://doi.org/10.1007/s00521-018-03965-1.   DOI
81 Zhang, W., Wu, C., Zhong, H., Li, Y. and Wang, L. (2021b), "Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization", Geosci. Front., 12(1), 469-477. https://doi.org/10.1016/j.gsf.2020.03.007.   DOI