Abdi, Y., Taheri-Garavand, A., Zarei-Sahamieh, A (2018) Prediction of strength parameters of sedimentary rocks using artificial neural networks and regression analysis. Arabian Journal of Geosciences, 11:587. https://doi.org/10.1007/s12517-018-3929-0.
Ahmadi, M. A., Ebadi, M., Shokrollahi, A., Majidi, SMJ (2013) Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir. Applied Soft Computing, 13: 1085–1098.
Atkinson, P. M., Tatnall, ARL (1997) Neural networks in remote sensing. International Journal of Remote Sensing, 18: 699–709.
Bejarbaneh, B. Y., Bejarbaneh, E. Y., Amin, M. F. M., Fahimifar, A., Jahed Armaghani, D., Majid, MZA (2018) Intelligent modelling of sandstone deformation behaviour using fuzzy logic and neural network systems. Bulletin of Engineering Geology and the Environment, 77: 345–361.
Cabalar, A. F., Cevik, A., Gokceoglu, C (2012) Some applications of adaptive neuro-fuzzy inference system (ANFIS) in geotechnical engineering. Computer and Geotechnics, 40: 14–33.
Dehghan, S., Sattari, G. H., Chehre-Chelgani, S., Aliabadi, MA (2010) Prediction of uniaxial compressive and modulus of elasticity for travertine sample using regression and artificial neural networks. International journal of Mining Science and Technology, 20: 41–46.
ISRM (1981) Rock characterization, testing and monitoring, ISRM suggested methods. International Society for Rock Mechanics, 211pp.
Kahraman, S., Gunaydin, O., Alber, M., Fener, M (2009) Evaluating the strength and deformability properties of misis fault breccia using artificial neural networks. Expert Systems Applications, 36: 6874–6878.
Lindquist, E. S., Goodman, RE (1994) Strength and deformation properties of a physical model melange. In: Nelson PP, Laubach SE (ed) Proceedings of the 1st North American rock mechanics symposium. Balkema, Rotterdam, 843–850.
Majdi, A., Beiki, M (2010) Evolving neural network using a genetic algorithm for predicting the deformation modulus of rock masses. International Journal of Rock Mechanics and Mining Sciences, 47: 246–253.
Majidi, A., Rezaei, M (2013) Prediction of unconfined compressive strength of rock surrounding a roadway using artificial neural network. Neural Computing & Applications, 23: 381–389.
Ozcelik, Y., Bayram, F., Yasitli, NE (2013) Prediction of engineering properties of rocks from microscopic data. Arabian Journal of Geosciences, 6: 3651–3668.
Rajesh-Kumar, B., Vardhan, H., Govindaraj, M., Vijay, GS (2013) Regression analysis and ANN models to predict rock properties from sound levels produced during drilling. International Journal of Rock Mechanics and Mining Sciences, 58: 61–72.
Sarkar, K., Tiwary, A., Singh, TN (2010) Estimation of strength parameters of rock using artificial neural networks. Bulletin of Engineering Geology and the Environment, 69: 599–606.
Singh, R., Kainthola, A., Singh, TN (2012) Estimation of elastic constant of rocks using an ANFIS approach. Applied Soft Computeing, 12(1): 40–45.
Singh, T.N., Dubey, RK (2000) A study of transmission velocity of primary wave (P-Wave) in Coal Measures sandstone. Journal of Scientific and Industrial Research, 59: 482–486.
Sonmez, H., Gokceoglu, C., Nefeslioglu, HA., Kayabasi, A (2006) Estimation of rock modulus: for intact rocks with an artificial neural network and for rock masses with a new empirical equation. International Journal of Rock Mechanics and Mining Sciences, 43: 224–235.
Taheri-Garavand, A., Ahmadi, H., Omid, M., Mohtasebi, S.S., Mollazade, K., Russell-Smith, J.R., Carlomagno, GM (2015) An intelligent approach for cooling radiator fault diagnosis based on infrared thermal image processing technique. Applied Thermal Engineering, 87: 434-443.
Tiryaki, B (2008) Predicting intact rock strength for mechanical excavation using multivariate statistics, artificial neural networks and regression trees. Engineering Geology, 99: 51–60.
Tonnizam-Mohamad, E., Jahed-Armaghani, D., Momeni, E., Alavi-Nezhad-Khalil-Abad, SV (2015) Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach. Bulletin of Engineering Geology and the Environment, 74: 745–757.
Torabi-Kaveh, M., Naseri, F., Sanei, S., Sarshari, B (2014) Application of artificial neural networks and multivariate statistics to predict UCS and E using physical properties of Asmari limestones. Arabian Journal of Geosciences, 8 (5): 2889-2897.
Yagiz, S., Sezer, E. A., Gokceoglu, C (2012) Artificial neural networks and nonlinear regression techniques to assess the influence of slake durability cycles on the prediction of uniaxial compressive strength and modulus of elasticity for carbonate rocks. International Journal for Numerical and Analytical Methods in Geomechanics, 36 (14): 1636-1650.
Yesiloglu-Gultekin, N., Gokceoglu, C., Sezer, EA (2013) Prediction of uniaxial compressive strength of granitic rocks by various nonlinear tools and comparison of their performances. International Journal of Rock Mechanics and Mining Sciences, 62: 113–122.
Yılmaz, I., Yuksek, AG (2008) An example of artificial neural network (ANN) application for indirect estimation of rock parameters. Rock Mechanics and Rock Engineering, 41(5): 781–795.
Yilmaz, I., Yuksek, AG (2009) Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN and ANFIS models. International Journal of Rock Mechanics and Mining Sciences, 46(4): 803–810.