[1] Z. Liu, J. Shao, W. Xu, Q. Wu, Indirect estimation of unconfined compressive strength of carbonate rocks using extreme learning machine, Acta Geotech. 10 (2015) 651–663. https://doi.org/10.1007/s11440-014-0316-1.
[2] E.T. Mohamad, D.J. Armaghani, E. Momeni, A.H. Yazdavar, M. Ebrahimi, Rock strength estimation: a PSO-based BP approach, Neural Comput. Appl. 30 (2018) 1635–1646. https://doi.org/10.1007/s00521-016-2728-3.
[3] M. Liang, E.T. Mohamad, R.S. Faradonbeh, D. Jahed Armaghani, S. Ghoraba, Rock strength assessment based on regression tree technique, Eng. Comput. 32 (2016) 343–354. https://doi.org/10.1007/s00366-015-0429-7.
[4] S. Charhate, M. Subhedar, N. Adsul, Prediction of concrete properties using multiple linear regression and artificial neural network, J. Soft Comput. Civ. Eng. 2 (2018) 27–38.
[5] E. Momeni, D. Jahed Armaghani, M. Hajihassani, M.F. Mohd Amin, Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks, Measurement. 60 (2015) 50–63. https://doi.org/10.1016/j.measurement.2014.09.075.
[6] B.Y. Bejarbaneh, E.Y. Bejarbaneh, M.F.M. Amin, A. Fahimifar, D. Jahed Armaghani, M.Z.A. Majid, Intelligent modelling of sandstone deformation behaviour using fuzzy logic and neural network systems, Bull. Eng. Geol. Environ. 77 (2018) 345–361. https://doi.org/10.1007/s10064-016-0983-2.
[7] K. Behzadafshar, M.E. Sarafraz, M. Hasanipanah, S.F.F. Mojtahedi, M.M. Tahir, Proposing a new model to approximate the elasticity modulus of granite rock samples based on laboratory tests results, Bull. Eng. Geol. Environ. 78 (2019) 1527–1536. https://doi.org/10.1007/s10064-017-1210-5.
[8] R.K. Umrao, L.K. Sharma, R. Singh, T.N. Singh, Determination of strength and modulus of elasticity of heterogenous sedimentary rocks: An ANFIS predictive technique, Measurement. 126 (2018) 194–201. https://doi.org/10.1016/j.measurement.2018.05.064.
[9] C. Gokceoglu, K. Zorlu, A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock, Eng. Appl. Artif. Intell. 17 (2004) 61–72. https://doi.org/10.1016/j.engappai.2003.11.006.
[10] B. Rajesh Kumar, H. Vardhan, M. Govindaraj, G.S. Vijay, Regression analysis and ANN models to predict rock properties from sound levels produced during drilling, Int. J. Rock Mech. Min. Sci. 58 (2013) 61–72. https://doi.org/10.1016/j.ijrmms.2012.10.002.
[11] N. Madhubabu, P.K. Singh, A. Kainthola, B. Mahanta, A. Tripathy, T.N. Singh, Prediction of compressive strength and elastic modulus of carbonate rocks, Measurement. 88 (2016) 202–213. https://doi.org/10.1016/j.measurement.2016.03.050.
[12] M. Beiki, A. Majdi, A.D. Givshad, Application of genetic programming to predict the uniaxial compressive strength and elastic modulus of carbonate rocks, Int. J. Rock Mech. Min. Sci. 63 (2013) 159–169.
[13] U. Atici, Modelling of the Elasticity Modulus for Rock Using Genetic Expression Programming, Adv. Mater. Sci. Eng. 2016 (2016) 1–8. https://doi.org/10.1155/2016/2063987.
[14] G. Hosseini, Capacity Prediction of RC Beams Strengthened with FRP by Artificial Neural Networks Based on Genetic Algorithm, J. Soft Comput. Civ. Eng. 1 (2017) 93–98.
[15] W. Liang, C. Yang, Y. Zhao, M.B. Dusseault, J. Liu, Experimental investigation of mechanical properties of bedded salt rock, Int. J. Rock Mech. Min. Sci. 44 (2007) 400–411. https://doi.org/10.1016/j.ijrmms.2006.09.007.
[16] L. Ma, X. Liu, M. Wang, H. Xu, R. Hua, P. Fan, S. Jiang, G. Wang, Q. Yi, Experimental investigation of the mechanical properties of rock salt under triaxial cyclic loading, Int. J. Rock Mech. Min. Sci. 62 (2013) 34–41. https://doi.org/10.1016/j.ijrmms.2013.04.003.
[17] H.B. Li, J. Zhao, T.J. Li, Triaxial compression tests on a granite at different strain rates and confining pressures, Int. J. Rock Mech. Min. Sci. 36 (1999) 1057–1063.
[18] E. Hoek, E.T. Brown, Empirical strength criterion for rock masses, J. Geotech. Geoenvironmental Eng. 106 (1980).
[19] V.K. Arora, Strength and deformational behaviour of jointed rocks, (1987).
[20] M.R. Asef, D.J. Reddish, The impact of confining stress on the rock mass deformation modulus, Géotechnique. 52 (2002) 235–241. https://doi.org/10.1680/geot.2002.52.4.235.
[21] ISRM Rock characterization testing and monitoring. In Brown, E.T. (Ed.), ISRM Suggested Methods. Pergamon, Oxford; 1981., (n.d.).
[22] ASTM Annual Book of ASTM Standards-Natural Building Stones; Soil and Rock, Part 19. ASTM Publication Office, Philadelphia, PA; 1980., (n.d.).
[23] U. Atici, Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network, Expert Syst. Appl. 38 (2011) 9609–9618. https://doi.org/10.1016/j.eswa.2011.01.156.
[24] C. Ferreira, Gene expression programming: a new adaptive algorithm for solving problems, ArXiv Prepr. Cs/0102027. (2001).
[25] C. Kayadelen, O. Günaydın, M. Fener, A. Demir, A. Özvan, Modeling of the angle of shearing resistance of soils using soft computing systems, Expert Syst. Appl. 36 (2009) 11814–11826. https://doi.org/10.1016/j.eswa.2009.04.008.
[26] I.F. Kara, Prediction of shear strength of FRP-reinforced concrete beams without stirrups based on genetic programming, Adv. Eng. Softw. 42 (2011) 295–304. https://doi.org/10.1016/j.advengsoft.2011.02.002.
[27] C. Ferreira, Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence. Springer, 2006.