Abdelhedi, M., Jabbar, R., Mnif, T., & Abbes, C., 2020. Prediction of uniaxial compressive strength of carbonate rocks and cement mortar using artificial neural network and multiple linear regressions. Acta Geodynamica et Geomaterialia, 17(3): 367-377.
Abdi, Y., Ghasemi Dehnavi, A., 2018. Prediction of uniaxial compressive strength and elastic modulus of sandstones using artificial neural network and multivariate regression analysis. New Findings of Applied Geology, Volume 13, Number 26: 45-54.
Ajal Luian, R., Mansoori, H., Mohammadi, M., 2013. Prediction of the elastic modulus of limestone using multivariate regression and artificial neural network. Journal of the Iranian Engineering Geological Society, Volume 5, Number 3: 33-38.
Akbari Poursalimi, Sh., Nikfar, Maryam., 2017. Prediction of urban development using sentinel satellite images by neural network method. Technology Quarterly in Air and Space Engineering, Volume 2, Number 3: 13-22.
Amanpour, S., Soleimani Rad, A., Keshtkar, L., Mokhtari Chelche, S., 2013. estimation of Ahvaz city housing price using neural network. scientific-research quarterly of economics and urban management. Volume 3, Number 9: 45-57.
Aras, A., Özşen, H., Dursun, A., 2019. Using Artificial Neural Networks for the Prediction of Bond Work Index from Rock Mechanics Properties. Mineral Processing and Extractive Metallurgy Review, 1–8.
Ashtoklin, Y., Nabavi, M., 1349. Geological map 1:250000 of Beshravieh Quadrangle. Barham, W., Rababah, S., Aldeeky, H., Hattamleh, O. H., 2020. Mechanical and Physical Based Artificial Neural Network Models for the Prediction of the nconfined Compressive Strength of Rock. Geotechnical and Geological Engineering, 38(5): 4779-4792.
Bieniawski Z.T., 1974. Estimating the strength of rock materials. Journal of the Southern African Geopersia 2024, 14(1): 45-62 61 Institute of Mining and Metallurgy. 74: 312-320.
Bieniawski, Z.T., 1976. Rock mass classification in rock engineering. Explor Rock Eng, Proc Symp. 1.97–106.
Cargill, J.S., Shakoor, A., 1990. Evaluation Of Empirical Methods For Measuring The Uniaxial Compressive Strength Of Rock. Int J Rock Mech Min Sci & Geomech Abstr, 27:495-503.
Deere D., Miller R., 1966. Engineering classification and index properties of intact rock.Technical Report No. AFNL-TR.65-116.
Ebdali, M., Khorasani, E., Salehin, S., 2020. A comparative study of various hybrid neural networks and regression analysis to predict unconfined compressive strength of travertine. Innovative Infrastructure Solutions. 5: 1-14.
Emami, M., Yathrabi, S., 2014. Application of artificial neural network in the interpretation of barometric test results. Madras Civil Engineering. Volume 14. Number 20. 11-25.
Esmaili, M., Pashandi, M., Hashemi Esfahanian, Mahmoud., 2016. Estimation of modulus of elasticity of virgin rock using artificial neural network and nonlinear regression. Advanced Applied Geology. Volume 6, Number 3: 10-28.
Ferentinou, M, Fakir, M., 2017. An ANN approach for the prediction of uniaxial compressive strength, of some sedimentary and igneous rocks in Eastern KwaZulu-Natal. International Society for Rock Mechanics and Rock Engineering. Ostrava.
Fursich, F.T., Wilmsen, M., Seyed-Emami, K., Majidfard, M.R., 2003. evidence of Synsedimentary Tectonics in the Northern Tabas Block, East-Central Iran: The Callovian (Middle Jurassic) Sikor Formation-Facies. 48: 151-170.
Ghafoori .M., Rastegarnia .A., Lashkaripour. G.R., 2018. Estimation of static parameters based on dynamical and physical properties in limestone rocks. Journal of African Earth Sciences.1.
Hacıefendioğlu, K., Genc, A. F., Nayır, S., Ayas, S., Altunışık, A. C., 2022. Automatic Estimation of Post-fire Compressive Strength Reduction of Masonry Structures Using Deep Convolutional Neural Network. Fire Technology. 58(5): 2779-2809.
Hagan, M. T., Demuth.H. B., Beale. M. H., Jesús. O. D., 2014. Neural Network Design, (2 edition), 1012 p.
Hallam, A., 2001. A review of the broad pattern of Jurassic sea-level changes and their possible causes in the light of current knowledge, Paleogeography. Paleoclimatology, Paleoecology. 167: 23-37.
Hassan, M. Y., & Arman, H., 2022. Several machine learning techniques comparison for the prediction of the uniaxial compressive strength of carbonate rocks. Scientific Reports, 12(1).20969.
Heydari, M., Rafiei, B., Nouri, M., Khanleri, G., Momeni, A., 2014. Estimation of uniaxial compressive strength and modulus of elasticity of conglomerate samples using regression and artificial neural network. Geotechnical Geology Journal. 1(3): 35-46.
Huang, M., Peng, G., Zhang, J. And Zhang, S., 2006. Application Of Artificial Neural Networks To The Prediction Of Dust Storms In Northwest China. Journal Of Global And Planetary Change .52: 216 – 224.
ISRM, 1981. Rock Characterization, Testing and Monitoring. In: Brown, E.T. (Ed.). ISRM suggested methods. Pergamon Press, Oxford. ISRM, 2007. The complete ISRM suggested methods for rock characterization, testing and monitoring, In: Ulusay R, Hudson JA (eds). Suggested methods prepared by the commission on testing methods.
Jafaripour,f., Hafizi Moghads, N., Lashkaripour,G R., 2015. engineering geological investigation of limestone decorative stones (case study of Mashhad city). I National Geology and Resource Exploration Conference
Jin, X., Zhao, R., Ma, Y., 2022. Application of a Hybrid Machine Learning Model for the Prediction of Compressive Strength and Elastic Modulus of Rocks, Minerals, 12(12):1506.
Minhaj, M.B., (1379), Computational intelligence, first volume: Basics of neural networks. first edition, Amirkabir University of Technology. Tehran. 502.
Moradi, M., Lashkaripur, GH., Ghafouri, M., 2016. Investigating the effect of durability index on the mechanical properties of Tirgan formation limestones, 5th National Conference of Applied Researches in Civil Engineering, Architecture and Urban Management, Tehran, Khwaja Nasiruddin Tousi University of Technology.
Motahari, M., 1400. Investigating the effect of index parameters on the static properties of limestone in dry and saturated conditions using artificial neural network. New findings of Applied Engineering 62 Sharifi et al. Geology. Volume 15 (30): 113-95.
Moussas, V. C., & Diamantis, K., 2021. Predicting uniaxial compressive strength of serpentinites through physical, dynamic and mechanical properties using neural networks. Journal of Rock Mechanics and Geotechnical Engineering. 13(1): 167-175.
Norusis, m.j., 1994. SPSS Advanced Statistics Company. Chicago, Illinois, 606 p.
Pala, M., Erdogan O., Ahmet., O., Ishak Yuce, M., 2007. Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks.Construction and Building Materials 21: 384 –394
Rastegarnia, A., Teshnizi, E. S., Hosseini, S., Shamsi, H. and Etemadifar, M., 2018. Estimation of punch strength index and static properties of sedimentary rocks using neural networks in southwest Iran. Measurement. 128: 464-478.
Safari Farrokhd, S. Lashkaripur, Gh. R. Hafizi-moqds, N., 2018. Evaluation of the brittleness index 20S of limestone and investigation of factors affecting it in dry and saturated state. Journal of the Engineering Geological Society of Iran, volume 12, number 3, pages 21-36Atmospheric Environment. 215-222
Tan, M., Li, X., Xin, L., 2014. Intensity Of Dust Storms In China From 1980 To 2007: A New Definition Atm ospheric Environment. 215-222
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