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Evaluation of effective geomechanical parameters in rock mass cavability using different intelligent techniques | ||
International Journal of Mining and Geo-Engineering | ||
مقاله 10، دوره 58، شماره 3، آذر 2024، صفحه 307-313 اصل مقاله (1.09 M) | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22059/ijmge.2024.369958.595133 | ||
نویسندگان | ||
Behnam Alipenhani؛ Hassan Bakhshandeh Amnieh* ؛ Abbas Majdi | ||
School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran. | ||
چکیده | ||
The paper presents the results of a comprehensive investigation of the applicability of various intelligence methods for optimal prediction of rock mass caveability in block caving by using effective geomechanical parameters. However, due to the complexity of the prediction of rock mass cavability, artificial intelligence-based methods, including classification and regression tree (CART), support vector machines (SVM), and Artificial neural network (ANN), have been selected. For validating and comparing the results, common MVR was used. Because of the dependency of the modeling generality and accuracy on the number of data, we attempted to obtain an adequate database from the result of numerical modeling. The distinct element method (DEM) used to study the rock mass cavability. The results indicated that ANN is the most accurate modeling technique with a determination coefficient of 0.987 as compared with other aforesaid methods. Finally, the sensitivity analysis showed that joint spacing, friction angle, joint set number, and undercut depth are the most prevailing parameters of rock mass cavability. However, the joint dip has shown the minimum effect on rock mass cavability in block caving mining method. | ||
کلیدواژهها | ||
Block caving؛ Cavability؛ Jointed rock mass؛ Numerical modeling؛ Artificial intelligence techniques | ||
مراجع | ||
[1]. Alipenhani, B., Majdi, A., & Bakhshandeh Amnieh, H. (2022). Determination of caving hydraulic radius of rock mass in block caving method using numerical modeling and multivariate regression. Journal of Mining and Environment, 13(1), 217-233.
[2]. Alipenhani, B., Majdi, A., & Bakhshandeh Amnieh, H. (2022). Cavability assessment of rock mass in block caving mining method based on numerical simulation and response surface methodology. Journal of Mining and Environment, 13(2), 579-606.
[3]. Alipenhani, B., Bakhshandeh Amnieh, H., & Majdi, A. (2023). Application of Finite Element Method for Simulation of Rock Mass Caving Processes in Block Caving Method. International Journal of Engineering, 36(1), 139-151.
[4]. Alipenhani, B., Majdi, A, & Bakhshandeh Amnieh, H. (2023). Investigating mechanical and geometrical effects of joints on minimum caving span in mass caving method. International Journal of Mining and Geo-Engineering, 57(2), 223-229.
[5]. Mawdesley, C. A. (2002) Predicting rock mass cavability in block caving mines. University of Queensland.
[6]. K Suzuki Morales, FT Suorineni (2017) Using numerical modeling to represent parameters affecting cave mining. Underground Mining Technology.Australian Centre for Geomechanics, Perth,ISBN978-0-9924810-7-0.
[7]. Rafiee, R., Ataei, M., KhaloKakaie, R., Jalali, S. E., & Sereshki, F. (2016). A fuzzy rock engineering system to assess rock mass cavability in block caving mines. Neural Computing and Applications, 27, 2083-2094.
[8]. Laubscher, Cave Mining Handbook, (2000), pp. 1–138.
[9]. Mawdesley, C. A. (2002) Predicting rock mass cavability in block caving mines. University of Queensland.
[10]. Alipenhani, B., Bakhshandeh Amnieh, H., & Majdi, A. (2022b). Physical model simulation of block caving in jointed rock mass. International Journal of Mining and Geo-Engineering.
[11]. Alipenhani, B., Majdi, A., & Bakhshandeh Amnieh, H. (2024). Determination of the caving zone height using numerical and physical modeling based on the undercutting method, joint dip, and spacing, Journal of Mining and Environment.
[12]. Mohammadi, S., Ataei, M., & Kakaie, R. (2018). Assessment of the importance of parameters affecting roof strata cavability in mechanized longwall mining. Geotechnical and Geological Engineering, 36, 2667-2682.
[13]. Liu, H., Ren, F., He, R., Li, G., & Zhang, J. (2021). Application of fuzzy comprehensive assessment and rock engineering system to assess cavability in block caving mining and establishment of its regionalized model. Environmental Earth Sciences, 80, 1-13.
[14]. Rafiee, R., Ataei, M., Khalokakaie, R., Sereshki, F. (2015) Determination and Assessment of Parameters Influencing Rock Mass Cavability in Block Caving Mines Using the Probabilistic Rock Engineering System, Rock Mechanics and Rock Engineering, DOI: 10.1007/s00603-014-0614-9
[15]. Jabinpour, A., Yarahmadi Bafghi, A., Gholamnejad, J. (2018) Geostatistical modeling of rock mass cavability based on laubscher approach in Sechahoon Mine.
[16]. 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(2), 246-253.
[17]. Majdi, A., & Rezaei, M. (2013). Prediction of unconfined compressive strength of rock surrounding a roadway using artificial neural network. Neural Computing and Applications, 23, 381-389.
[18]. Beiki, M., Majdi, A., & Givshad, A. D. (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.
[19]. Rezaei, M., Majdi, A., & Monjezi, M. (2014). An intelligent approach to predict unconfined compressive strength of rock surrounding access tunnels in longwall coal mining. Neural Computing and Applications, 24, 233-241.
[20]. Hasanipanah, M., Amnieh, H. B., Arab, H., & Zamzam, M. S. (2018). Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting. Neural Computing and Applications, 30, 1015-1024.
[21]. Mehrdanesh, A., Monjezi, M., Khandelwal, M., & Bayat, P. (2021). Application of various robust techniques to study and evaluate the role of effective parameters on rock fragmentation. Engineering with Computers, 1-11.
[22]. Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational physics, 378, 686-707.
[23]. Li, Y., Hishamuddin, F. N. S., Mohammed, A. S., Armaghani, D. J., Ulrikh, D. V., Dehghanbanadaki, A., & Azizi, A. (2021). The effects of rock index tests on prediction of tensile strength of granitic samples: a neuro-fuzzy intelligent system. Sustainability, 13(19), 10541.
[24]. Vyazmensky, A., Elmo, D., & Stead, D. (2010). Role of rock mass fabric and faulting in the development of block caving induced surface subsidence. Rock mechanics and rock engineering, 43(5), 533-556.
[25]. Sainsbury, B. (2012). A model for cave propagation and subsidence assessment in jointed rock masses. University of South Wales.
[26]. Topal, E. (2008). Evaluation of a mining project using discounted cash flow analysis, decision tree analysis, Monte Carlo simulation and real options using an example. International Journal of Mining and Mineral Engineering, 1(1), 62-76.
[27]. Song, Y. Y., & Ying, L. U. (2015). Decision tree methods: applications for classification and prediction. Shanghai archives of psychiatry, 27(2), 130.
[28]. Wu, C. H., Ho, J. M., & Lee, D. T. (2004). Travel-time prediction with support vector regression. IEEE transactions on intelligent transportation systems, 5(4), 276-281.
[29]. Chen, G., Fu, K., Liang, Z., Sema, T., Li, C., Tontiwachwuthikul, P., & Idem, R. (2014). The genetic algorithm based back propagation neural network for MMP prediction in CO2-EOR process. Fuel, 126, 202-212 | ||
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