. Selmer-Olsen, R. and Lien, R. (1960). Bergartens borbarhet og sprengbarhet. Teknisk Ukeblad, 34, 3-11.
. Hossain, M.E. and Al-Majed, A.A. (2015). Fundamentals of sustainable drilling engineering. John Wiley & Sons.
. Eren, T. and Ozbayoglu, M.E. (2010). Real-time optimization of drilling parameters during drilling operations. In SPE oil and gas India conference and exhibition. Society of Petroleum Engineers.
. Payette, G.S., Spivey, B.J., Wang, L., Bailey, J.R., Sanderson, D., Kong, R. and Eddy, A. (2017). A Real-Time Well-Site based Surveillance and Optimization Platform for Drilling: Technology, Basic Workflows and Field Results. In SPE/IADC Drilling Conference and Exhibition. Society of Petroleum Engineers.
. Osgouei, R.E. (2007). Rate of Penetration Estimation Model for Directional and Horizontal Wells. Master’s Thesis, Middle East Technical University, Ankara, Turkey.
. Hoseinie, S.H., Ataei, M. and Mikaeil, R. (2019). Effects of microfabric on drillability of rocks. Bulletin of Engineering Geology and the Environment. 78 (3): 1443-1449.
. Soleimani, M. (2017). Well performance optimization for gas lift operation in a heterogeneous reservoir by fine zonation and different well type integration. Journal of Natural Gas Science and Engineering. 40: 277-287.
. Ataei, M., KaKaie, R., Ghavidel, M. and Saeidi, O. (2015). Drilling rate prediction of an open-pit mine using the rock mass drillability index. International Journal of Rock Mechanics and Mining Sciences. 73: 130-138.
. Shad, H.I.A., Sereshki, F., Ataei, M. and Karamoozian, M. (2018). Prediction of rotary drilling penetration rate in iron ore oxides using rock engineering system. International Journal of Mining Science and Technology. 28 (3): 407-413.
. Hoseinie, S.H., Ataei, M. and Mikaiel, R. (2012). Comparison of some rock hardness scales applied in drillability studies. Arabian Journal for Science and Engineering. 37 (5): 1451-1458.
. Kahraman, S., Balcı, C., Yazıcı, S. and Bilgin, N. (2000). Prediction of the penetration rate of rotary blast hole drills using a new drillability index. International Journal of Rock Mechanics and Mining Sciences. 37 (5): 729-743.
. Altindag, R. (2002). The evaluation of rock brittleness concept on rotary blast hold drills. Journal of the Southern African Institute of Mining and Metallurgy. 102 (1): 61-66.
. Bilgin, N. and Kahraman, S. (2003, June). Drillability prediction in rotary blast hole drilling. In Proc. 18th Int. Mining Congress and Exhibition of Turkey, Antalya, Turkey (pp. 177-182).
. Kahraman, S., Bilgin, N. and Feridunoglu, C. (2003). Dominant rock properties affecting the penetration rate of percussive drills. International Journal of Rock Mechanics and Mining Sciences. 40 (5): 711-723.
. Grima, M.A. and Babuška, R. (1999). Fuzzy model for the prediction of unconfined compressive strength of rock samples. International Journal of Rock Mechanics and Mining Sciences. 36 (3): 339-349.
. Hoseinie, S.H., Ataei, M. and Osanloo, M. (2009). A new classification system for evaluating rock penetrability. International Journal of Rock Mechanics and Mining Sciences. 46 (8): 1329-1340.
. Moradi, H., Bahari, M.H., Sistani, M.B.N. and Bahari, A. (2010). Drilling rate prediction using an innovative soft computing approach. Scientific Research and Essays. 5 (13): 1583-1588.
. Nandi, A.K. and Davim, J.P. (2009). A study of drilling performances with minimum quantity of lubricant using fuzzy logic rules. Mechatronics. 19 (2): 218-232.
. Hashmi, K., Graham, I.D. and Mills, B. (2000). Fuzzy logic-based data selection for the drilling process. Journal of Materials Processing Technology. 108 (1): 55-61.
. Khandelwal, M. and Armaghani, D.J. (2016). Prediction of drillability of rocks with strength properties using a hybrid GA-ANN technique. Geotechnical and Geological Engineering. 34 (2): 605-620.
. Feng, X. (1995). A neural network approach to comprehensive classification of rock stability, blastability and drillability. International Journal of Surface Mining and Reclamation. 9 (2): 57-62.
. Gamal, H., Elkatatny, S. and Abdulraheem, A. (2020, November). Rock Drillability Intelligent Prediction for a Complex Lithology Using Artificial Neural Network. In Abu Dhabi International Petroleum Exhibition & Conference. Society of Petroleum Engineers.
. Fattahi, H. and Bazdar, H. (2017). Applying improved artificial neural network models to evaluate drilling rate index. Tunneling and Underground Space Technology. 70: 114-124.
. Wang, L.N. and Feng, X.T. (1993, January). Comprehensive Classification Iof Rock Stability, Blastability and Drillability based on Neural Networks. In The 34th US Symposium on Rock Mechanics (USRMS). American Rock Mechanics Association.
. Gan, C., Cao, W., Wu, M., Chen, X., Lu, C., Hu, Y. and Wen, G. (2016). Intelligent Nadaboost-ELM modeling method for formation drillability using well logging data. Journal of Advanced Computational Intelligence and Intelligent Informatics. 20 (7): 1103-1111.
. Gan, C., Cao, W., Wu, M., Chen, X., Hu, Y., Wen, G. and Ding, H. (2017). An online modeling method for formation drillability based on OS-Nadaboost-ELM algorithm in deep drilling process. IFAC-PapersOnLine. 50 (1): 12886-12891.
. Tewari, S., Dwivedi, U.D. and Biswas, S. (2021). A Novel Application of Ensemble Methods with Data Resampling Techniques for Drill Bit Selection in the Oil and Gas Industry. Energies. 14 (2): 432.
. Li, S., Zhang, J., Wu, S., Chen, W., Chen, D., Li, X. and Wang, H. (2019, September). Prediction of Rate of Penetration based on Random Forest in Deep Well. In International Conference on Inforatmion technology in Geo-Engineering. (pp. 517-526). Springer, Cham.
. Mikaeil, R., Haghshenas, S.S. and Hoseinie, S.H. (2018). Rock penetrability classification using artificial bee colony (ABC) algorithm and self-organizing map. Geotechnical and Geological Engineering. 36 (2): 1309-1318.
. Basarir, H., Tutluoglu, L. and Karpuz, C. (2014). Penetration rate prediction for diamond bit drilling by adaptive neuro-fuzzy inference system and multiple regressions. Engineering Geology. 173: 1-9.
. Saeidi, O., Torabi, S.R., Ataei, M. and Rostami, J. (2014). A stochastic penetration rate model for rotary drilling in surface mines. International Journal of Rock Mechanics and Mining Sciences. 68: 55-65.
. Sagi, O. and Rokach, L. (2018). Ensemble learning: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 8 (4): e1249.
. Kotsiantis, S.B. (2013). Decision trees: a recent overview. Artificial Intelligence Review, 39(4), 261-283.
. Breiman, L. (2001). Random forests. Machine learning. 45 (1): 5-32.
. Freund, Y. and Schapire, R.E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of computer and system sciences. 55 (1): 119-139.
. Lyubartsev, A.P., Martsinovski, A.A., Shevkunov, S.V. and VorontsovVelyaminov, P.N. (1992). New approach to Monte Carlo calculation of the free energy: Method of expanded ensembles. The Journal of chemical physics. 96 (3): 1776-1783.
. Iba, Y. (2001). Extended ensemble Monte Carlo. International Journal of Modern Physics C, 12(05), 623-656.
. Yenice, H. (2019). Determination of Drilling Rate Index Based on Rock Strength Using Regression Analysis. Anais da Academia Brasileira de Ciências, 91 (3).
. Yenice, H., Özdoğan, M.V., and Özfırat, M.K. (2018). A sampling study on rock properties affecting drilling rate index (DRI). Journal of African Earth Sciences, 141, 1-8.
. Azizi, A., Shafaei, S.Z., Rooki, R., Hasanzadeh, A. and Paymard, M. (2012). Estimating of gold recovery by using back propagation neural network and multiple linear regression methods in cyanide leaching process.
. Williamson, D.F., Parker, R.A. and Kendrick, J.S. (1989). The box plot: a simple visual method to interpret data. Annals of internal medicine. 110 (11): 916-921.
. Witten, I.H. and Frank, E. (2005). Data Mining — Practical Machine Learning Tools and Techniques, Second Edition. Elsevier, Amsterdam.
. Friedl, M.A. and Brodley, C.E. (1997). Decision tree classification of land cover from remotely sensed data. Remote sensing of environment. 61 (3): 399-409.
. Pal, M. and Mather, P.M. (2003). An assessment of the effectiveness of decision tree methods for land cover classification. Remote sensing of environment. 86 (4): 554-565.
. Zhang, J., Li, D. and Wang, Y. (2020). Toward intelligent construction: prediction of mechanical properties of manufactured-sand concrete using tree-based models. Journal of Cleaner Production. 258: 120665.
. Sun, W. and Gao, Q. (2019). Exploration of energy saving potential in China power industry based on Adaboost back propagation neural network. Journal of Cleaner Production. 217: 257-266.
. Hong, H., Liu, J., Bui, D.T., Pradhan, B., Acharya, T.D., Pham, B.T. and Ahmad, B.B. (2018). Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China). Catena. 163: 399-413.
. Maniruzzaman, M., Rahman, M.J., Al-MehediHasan, M., Suri, H.S., Abedin, M.M., El-Baz, A. and Suri, J.S. (2018). Accurate diabetes risk stratification using machine learning: role of missing value and outliers. Journal of medical systems. 42 (5): 1-17.
. Zhang, J., Xu, J., Hu, X., Chen, Q., Tu, L., Huang, J. and Cui, J. (2017). Diagnostic method of diabetes based on support vector machine and tongue images. BioMed research international, 2017.
. Yang, P., Hwa Yang, Y., B Zhou, B. and Y Zomaya, A. (2010). A review of ensemble methods in bioinformatics. Current Bioinformatics. 5 (4): 296-308.
. Breiman, L. (1996). Bagging predictors. Machine learning. 24 (2): 123-140.
. Liang, W., Sari, A., Zhao, G., McKinnon, S.D. and Wu, H. (2020). Short-term rockburst risk prediction using ensemble learning methods. Natural Hazards, 104 (2): 1923-1946
. Wattimena, R.K., Kramadibrata, S., Sidi, I.D., Arif, I. and Azizi, M.A. (2012, January). Probabilistic analysis of single bench using new slope stability curves. In ISRM Regional Symposium-7th Asian Rock Mechanics Symposium. International Society for Rock Mechanics and Rock Engineering.
. Azizi, M.A., Kramadibrata, S., Wattimena, R.K. and Sidi, I.D. (2013). Probabilistic analysis of physical models slope failure. Procedia Earth and Planetary Science, 6: 411-418.
. Solver, F. (2010). Premium solver platform. User Guide, Frontline Systems.
. EPA, U. (1997). Environmental Protection Agency. Guiding principles for Monte Carlo analysis. EPA/630/R-97/001.
. Sari, M. and Ataei, M. (2012). Development of an empirical model for predicting the effects of controllable blasting parameters on fly-rock distance in surface mines. International Journal of Rock Mechanics and Mining Sciences, 52, 163-170.
. Steinfeld, B., Scott, J., Vilander, G., Marx, L., Quirk, M., Lindberg, J. and Koerner, K. (2015). The role of lean process improvement in implementation of evidence-based practices in behavioral health care. The Journal of Behavioral Health Services and Research, 42 (4): 504-518.
. Mahdiyar, A., Hasanipanah, M., Armaghani DJ et al A Monte Carlo technique in safety assessment of slope under seismic condition. Eng Comput. 2017. https ://doi.org/10.1007/s0036 6-016-0499-1
. Bianchini, F. and Hewage, K. (2012). Probabilistic social cost-benefit analysis for green roofs: A lifecycle approach. Building and Environment, 58: 152-162.
. Dunn, W.L. And Shultis, J.K. Monte Carlo methods for design and analysis of radiation detectors. Radiat Phys Chem. 2009; 78:852–858. https ://doi.org/10.1016/j.radph ysche m.2009.04.030.