اجل لوئیان، ر.، منصوری، ح.، محمدی، م.، (1391). پیش بینی مدول الاستیک سنگ آهک با استفاده از رگرسیون چند متغیره و شبکه عصبی مصنوعی. مجله انجمن زمین شناسی مهندسی ایران، پائیز و زمستان 1391، جلد پنجم، شماره 3 و 4، صفحه 33 تا 38.
احمدی، ر، امیری بختیار، م (۱۳۹۷). به کارگیری مدل رگرسیون ماشین بردار پشتیبان به منظور تخمین میزان اشباع شدگی آب سازند یکی از میدان های نفتی بزرگ جنوب غرب ایران. نشریه پژوهش های ژئوفیزیک کاربردی. ۴(۲) ۲۱۰-۱۹۹.
عبدی، ی.، قاسمی دهنوی، آ (1398). پیشبینی مقاومت فشاری تک محوری و مدول الاستیک ماسهسنگها با استفاده از شبکه عصبی مصنوعی و آنالیز رگرسیون چند متغیره. یافته های نوین زمین شناسی کاربردی، دوره 13ف شماره 26ف پائیز و زمستان 1398.
علیپور، ع.، مختاریان اصل، م.، اسدی زاده، م.، (1399). تخمین حفاری ویژه انفجار در تونلهای کوچکمقطع با استفاده از ماشین بردار پشتیبان. مجله انجمن زمین شناسی مهندسی ایران، جلد سیزدهم، شماره 1، صفحه 1 تا 13.
Abdi, Y., Garavand, A.T., Sahamieh, R.Z., 2018. Prediction of strength parameters of sedimentary rocks using artificial neural networks and regression analysis. Arabian Journal of Geosciences, 11:587.
Armaghani, D.J., Asteris, P.G., Fatemi, S.A., et al. 2020a. On the Use of Neuro-Swarm System to Forecast the Pile Settlement. Applied Sciences, 10:1904.
Armaghani, D.J., Faradonbeh, R.S., Momeni, E., et al. 2018. Performance prediction of tunnel boring machine through developing a gene expression programming equation. Engineering with Computer, 34:129–141.
Armaghani, D.J., Hatzigeorgiou, G.D., Karamani, C., et al. 2019. Soft computing-based techniques for concrete beams shear strength. Procedia Structural Integrity, 17:924–933.
Armaghani, D.J., Kumar, D., Samui, P., et al. 2020b. A novel approach for forecasting of ground vibrations resulting from blasting: modified particle swarm optimization coupled extreme learning machine. Engineering with Computer, https://doi.org/10.1007/s00366-020-00997-x.
Armaghani, D.J, Mohamad, E.T., Hajihassani, M., et al. 2016a. Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances. Engineering with Computer, 32:189–206.
Armaghani, D.J., Mohamad, E.T., Momeni, E., et al. 2016b. Prediction of the strength and elasticity modulus of granite through an expert artificial neural network. Arabian Journal of Geosciences, 9:48.
Beiki, M., Majdi, A., Givshad, A., 2013. Application of genetic programming to predict the uniaxial compressive strength and elastic modulus of carbonate rocks. International Journal of Rock Mechanics and Mining Sciences, 63:159-169.
Bejarbaneh, B.Y., Bejarbaneh, E.Y., Fahimifar, A., et al. 2018. Intelligent modelling of sandstone deformation behaviour using fuzzy logic and neural network systems. Bulletin of Engineering Geology and the Environment, 77:345–361.
Bunawan, A.R., Momeni, E., Armaghani, D.J., Rashid, A.S.A., 2018. Experimental and intelligent techniques to estimate bearing capacity of cohesive soft soils reinforced with soil-cement columns. Measurement, 124:529–538.
Ceryan, N., Okkan, U., Kesimal, A. 2012. Application of generalized regression neural networks in predicting the unconfined compressive strength of carbonate rocks. Rock Mechanics and Rock Engineering, 45:1055–1072.
Diamantis, K., Gartzos, E., Migiros, G., 2009. Study on uniaxial compressive strength, point load strength index, dynamic and physical properties of serpentinites from Central Greece: test results and empirical relations. Engineering Geology, 108:199–207.
Fang, Q., Bejarbaneh, B.Y., Vatandoust, M., et al. 2019. Strength evaluation of granite block samples with different predictive models. Engineering with Computer, https://doi.org/10.1007/s00366-019-00872.
Gordan, B., Armaghani, D.J., Adnan, A.B., Rashid, A.S.A., 2016. A New Model for Determining Slope Stability Based on Seismic Motion Performance. Soil Mechanics and Foundation Engineering, 53:344–351. doi: 10.1007/s11204-016-9409-1.
Hajihassani, M., Abdullah, S.S., Asteris, P.G., Armaghani, D.J., 2019. A Gene Expression Programming Model for Predicting Tunnel Convergence. Applied Sciences, 9:4650.
Hajihassani, M., Jahed Armaghani, D., Marto, A., Tonnizam Mohamad, E., 2015. Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bulletin of Engineering Geology and the Environment, 74:. doi: 10.1007/s10064-014-0657-x.
Han, H., Armaghani, D.J., Tarinejad, R., et al. 2020. Random Forest and Bayesian Network Techniques for Probabilistic Prediction of Flyrock Induced by Blasting in Quarry Sites. Natural Resources Research, https://doi.org/10.1007/s11053-019-09611-4.
Hasanipanah, M., Monjezi, M., Shahnazar, A., Jahed Armaghani, Danial., Farazmand, A., 2015. Feasibility of indirect determination of blast induced ground vibration based on support vector machine. Measurement 75: 289–297.
ISRM (1981) Rock characterization, testing and monitoring, ISRM suggested methods. Int Soc for Rock Mech 211pp
Ivakhnenko, A.G., 1968. The group method of data of handling; a rival of the method of stochastic approximation. Sov Autom Control, 13:43–55.
Jahed Armaghani, D., Tonnizam Mohamad, E., Hajihassani, M., et al. 2016. Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods. Engineering with Computers, 32: 109-121. doi: 10.1007/s00366-015-0402-5.
Jahed Armaghani, D., Mohd Amin, M.F., Yagiz, S., Faradonbeh, R.S., Abdullah, R.A. 2016. Prediction of the uniaxial compressive strength of sandstone using various modeling techniques. International Journal of Rock Mechanics and Mining Sciences, 85, 174–186.
Kaastra I, Boyd M (1996) Designing a neural network for forecasting financial and economic time series. Neurocomputing, 10:215–236.
Kahraman, S., Gunaydin, O., Fener, M., 2005. The effect of porosity on the relation between uniaxial compressive strength and point load index. International Journal of Rock Mechanics and Mining Sciences, 42:584–589.
Khandelwal, M., 2013. Correlating P-wave velocity with the physico-mechanical properties of different rocks. Pure Applied Geophysics, 170:507–514.
Khandelwal, M., Armaghani, D.J., Faradonbeh, R.S., et al. 2017. Classification and regression tree technique in estimating peak particle velocity caused by blasting. Engineering with Computers, 33:45–53.
Khandelwal, M., Monjezi, M., 2013. Prediction of backbreak in open-pit blasting operations using the machine learning method. Rock Mechanics and rock Engineering, 46:389–396.
Khandelwal, M., Singh, T.N., 2009. Correlating static properties of coal measures rocks with P-wave velocity. International Journal of Coal Geology, 79:55–60.
Koopialipoor, M., Nikouei, S.S., Marto, A., et al. 2018b. Predicting tunnel boring machine performance through a new model based on the group method of data handling. Bulletin of Engineering Geology and the Environment, 78:3799–3813.
Li, D., Jahed Armaghani, D., Zhou, J., Lai, S.H., Hasanipanah, M., 2020. A GMDH Predictive Model to Predict Rock Material Strength Using Three Non-destructive Tests. Journal of Nondestructive Evaluation, 39:81.
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.
Marto, A., Hajihassani, M., Momeni, E., 2014. Bearing Capacity of Shallow Foundation’s Prediction through Hybrid Artificial Neural Networks. In: Applied Mechanics and Materials, Trans Tech Publ, pp 681–686.
Masters T (1994) Practical neural network recipes in C++. Academic Press, Boston MA
Mohamad, E.T., Armaghani, D.J., Ghoroqi, M., et al. 2017a. Ripping Production Prediction in Different Weathering Zones According to Field Data. Geotechnical and Geological Engineering, 35:2381–2399. doi: 10.1007/s10706-017-0254-4.
Mohamad, E.T., Faradonbeh, R.S., Armaghani, D.J., et al. 2017b. An optimized ANN model based on genetic algorithm for predicting ripping production. Neural Computing & Applications, 28:393–406.
Mohamad, E.T., Jahed Armaghani, D., Momeni, E., Alavi Nezhad Khalil Abad, S.V., 2014. Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach. Bulletin of Engineering Geology and the Environment, 74: 745-757. doi: 10.1007/s10064-014-0638-0.
Momeni, E., Armaghani, D.J., Fatemi, S.A., Nazir, R., 2018. Prediction of bearing capacity of thin-walled foundation: a simulation approach. Engineering with Computers, 34:319–327.
Momeni, E., Armaghani, D.J., Hajihassani, M., Amin, M.F.M., 2015a. Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks. Measurement, 60:50–63.
Momeni, E., Nazir, R., Armaghani, D.J., Maizir, H., 2015b. Application of artificial neural network for predicting shaft and tip resistances of concrete piles. Earth Science Reseach Journal, 19:85–93.
Momeni, E., Nazir, R., Armaghani, D.J., Mohamad, E.T., 2015c. Prediction of unconfined compressive strength of rocks: a review paper. Jurnal Teknologi 77(11): 11-2015.
Moradian, Z.A., Behnia, M., 2009. Predicting the uniaxial compressive strength and static Young’s modulus of intact sedimentary rocks using the ultrasonic test. International Journal of Geomechanics, 9:14–19.
Najafzadeh, M., Barani, G.A., Azamathulla, H.M., 2013. GMDH to predict scour depth around a pier in cohesive soils. Applied Ocean Research, 40:35–41.
Nazir, R., Momeni, E., Armaghani, D. J., Amin, M.M., 2013. Correlation between unconfined compressive strength and indirect tensile strength of limestone rock samples. Electronic Journal of Geotechnical Engineering, 18(1), 1737-1746.
Negara, A., Ali, S., AlDhamen, A., Kesserwan, H., & Jin, G., 2017. Unconfined Compressive Strength Prediction from Petrophysical Properties and Elemental Spectroscopy Using Support-Vector Regression. In SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition. Society of Petroleum Engineers.
Nelson, M.M., Illingworth, W.T., 1991. A practical guide to neural nets. Addison-Wesley Reading, MA.
Priddy, K.L., Keller, P.E., 2005. Artificial neural networks: an introduction. SPIE press.
Rezaei, H., Nazir, R., Momeni, E., 2016. Bearing capacity of thin-walled shallow foundations: an experimental and artificial intelligence-based study. Journal of Zhejiang University-SCIENCE A, 17:273–285.
Singh, R., Kainthola, A., Singh, T.N., 2012 Estimation of elastic constant of rocks using an ANFIS approach. Applied Soft Computing, 12:40–45.
Smola, A.J., Schölkopf, B., 2004. A tutorial on support vector regression. Statistics and Computing 14, 199–222 (2004).
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., Hajihassani, M., Jahed Armaghani, D., Marto, A., 2014. Simulation of blasting-induced air overpressure by means of Artificial Neural Networks. International Review on Modelling and Simulations, 5(6):2501-2506.
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.
Ulusay R, Hudson JA ISRM (2007) The complete ISRM suggested methods for rock characterization, testing and monitoring: 1974–2006. Comm Test methods Int Soc Rock Mech Compil arranged by ISRM Turkish Natl Group, Ankara, Turkey 628:
Xu, H., Zhou, J.G., Asteris, P., et al. 2019. Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate. Applied Sciences, 9:3715.
Yang, H., Liu, J., Liu, B., 2018. Investigation on the cracking character of jointed rock mass beneath TBM disc cutter. Rock Mechanics and Rock Engineering, 51:1263–1277.
Yesiloglu-Gultekin, N., Gokceoglu, C., Sezer, E.A., 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. doi: 10.1016/j.ijrmms.2013.05.005.
Yilmaz, I., Yuksek, G., 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, 46:803–810.
Yılmaz, I., Yuksek, A., 2008. An example of artificial neural network (ANN) application for indirect estimation of rock parameters. Rock Mech Rock Eng, 41: 781-795.
Yu, H. and Kim, S., 2012. SVM tutorial: classification, regression, and ranking, Handbook of Natural
Computing, Springer Berlin Heidelberg, 479-506
Zhang, H., Zhou, J., Armaghani, D.J., et al. 2020. A Combination of Feature Selection and Random Forest Techniques to Solve a Problem Related to Blast-Induced Ground Vibration. Applied Sciences, 10:869.
Zhou, J., Aghili, N., Ghaleini, E.N., et al. 2019a. A Monte Carlo simulation approach for effective assessment of flyrock based on intelligent system of neural network. Engineering with Computers, https://doi.org/10.1007/s00366-019-00726-z.
Zhou, J., Li, E., Yang, S., et al. 2019b. Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories. Safety Science, 118:505–518.
Zhou, J., Shi, X., Li, X., 2016. Utilizing gradient boosted machine for the prediction of damage to residential structures owing to blasting vibrations of open pit mining. Journal of Vibration and Control, 22:3986–3997.
Vapnik, V. and Lerner, A. 1963. Pattern Recognition using Generalized Portrait Method. Automation and Remote Control 24: 774-780
Vapnik, V., Golowich, S., and Smola, A. 1997. Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing. In Advances in Neural Information Processing Systems 9, edition M. C. Mozer, M. I. Jordan, and T Petsche, 281-287, Cambridge, Massachusetts: MIT Press.
)