Numerical Analysis and Predictive Modeling Using Artificial Intelligence of the Relaxation Zone Around Hangingwall of Sublevel Open Stopes in Underground Mines

Amine, Soufi; Youssef, Zerradi; Mohamed, Soussi; Latifa, Ouadif; Anas, Bahi

doi:10.22044/jme.2024.14413.2700

Document Type : Original Research Paper

Authors

Mohammed V University in Rabat, Morocoo

10.22044/jme.2024.14413.2700

Abstract

The aim of this study is to thoroughly analyze the relaxation zone developing around sublevel stopes in underground mines and identify the main parameters controlling its extent. A numerical approach based on the finite element method, combined with the Hoek-Brown failure criterion, was implemented to simulate various geometric configurations, geological conditions, and in-situ stress states. A total of 425 simulations were carried out by varying depth, horizontal-to-vertical stress ratio (k), rock mass quality (RMR), foliation orientation and spacing, as well as the height, width, and inclination of the sublevels. The results enabled the development of robust predictive models using regression analysis techniques and artificial neural networks (ANNs) to estimate the extent of the relaxation zone as a function of the different input parameters. It was demonstrated that depth and the k ratio significantly influence the extent of the relaxation zone. Additionally, a decrease in rock mass quality leads to a substantial increase in this zone. Structural characteristics, such as foliation orientation and spacing, also play a decisive role. Finally, the geometric parameters of the excavations, notably the height, width, and inclination of the sublevels, directly impact stress redistribution and the extent of the relaxation zone. The overall ANN model, taking into account all these key parameters, exhibited high accuracy with a correlation coefficient of 0.97. These predictive models offer valuable tools for optimizing the design of underground mining operations, improving operational safety, and increasing productivity.

Keywords

Main Subjects

Simulation & Optimization

References

[1]. Hudson, J. A. (1993). Comprehensive rock engineering: principles, practice and projects. Volume 3. Rock testing and site characterization.

[2]. Soufi, A., Bahi, L., Ouadif, L., & Kissai, J. E. (2018). Correlation between rock mass rating, Q-system and rock mass index based on field data. In MATEC Web of Conferences (Vol. 149, p. 02030). EDP Sciences.

[3]. Soufi, A., Bahi, L., Ouadif, L., (2018). Adjusted anisotropic strength model for meta-siltstones and prediction of ucs from indirect tensile tests. Int. J. Civ. Eng. Technol., 9(7), 598-611.

[4]. Yaylacı, M., Abanoz, M., Yaylacı, E. U., Ölmez, H., Sekban, D. M., & Birinci, A. (2022). Evaluation of the contact problem of functionally graded layer resting on rigid foundation pressed via rigid punch by analytical and numerical (FEM and MLP) methods. Archive of Applied Mechanics, 92(6), 1953-1971.

[5]. Yaylacı, M., Abanoz, M., Yaylacı, E. U., Ölmez, H., Sekban, D. M., & Birinci, A. (2022). The contact problem of the functionally graded layer resting on rigid foundation pressed via rigid punch.

[6]. Yaylacı, M., Şabano, B. Ş., Özdemir, M. E., & Birinci, A. (2022). Solving the contact problem of functionally graded layers resting on a HP and pressed with a uniformly distributed load by analytical and numerical methods. Structural Engineering and Mechanics, An Int'l Journal, 82(3), 401-416.

[7]. Turan, M., Uzun Yaylacı, E., & Yaylacı, M. (2023). Free vibration and buckling of functionally graded porous beams using analytical, finite element, and artificial neural network methods. Archive of Applied Mechanics, 93(4), 1351-1372.

[8]. Yaylacı, E. U., Öner, E., Yaylacı, M., Özdemir, M. E., Abushattal, A., & Birinci, A. (2022). Application of artificial neural networks in the analysis of the continuous contact problem. Structural Engineering and Mechanics, An Int'l Journal, 84(1), 35-48.

[9]. Yaylacı, M., Yaylacı, E. U., Özdemir, M. E., Öztürk, Ş., & Sesli, H. (2023). Vibration and buckling analyses of FGM beam with edge crack: Finite element and multilayer perceptron methods.

[10]. Özdemir, M. E., & Yaylac, M. (2023). Research of the impact of material and flow properties on fluid-structure interaction in cage systems. Wind and structures, 36(1), 31.

[11]. Yaylaci, E. U., Yaylaci, M., Ozdemir, M. E., Terzi, M., & Ozturk, S. (2023). Analyzing the mechano-bactericidal effect of nano-patterned surfaces by finite element method and verification with artificial neural networks. Advances in nano research, 15(2), 165-174.

[12]. Yaylacı, M., Şabano, B. Ş., Özdemir, M. E., & Birinci, A. (2022). Solving the contact problem of functionally graded layers resting on a HP and pressed with a uniformly distributed load by analytical and numerical methods. Structural Engineering and Mechanics, An 'nt'l Journal, 82(3), 401-416.

[13]. Sarfarazi, V., Haeri, H., Fatehi Marji, M., Saeedi, G., & Namdarmanesh, A. (2023). Investigation of Shear Properties of Open Non-persistent Latitudinal Discontinuities of Same Level. Journal of Mining and Environment, 14(4), 1361-1371.

[14]. Rezaei, A., Sarfarazi, V., Babanouri, N., Omidi Manesh, M., & Jahanmiri, S. (2023). Failure Mechanism of Rock Pillar Containing Two Edge Notches: Experimental Test and Numerical Simulation. Journal of Mining and Environment, 14(3), 961-971.

[15]. Omidi Manesh, M., Sarfarazi, V., Babanouri, N., & Rezaei, A. (2023). Investigation of External Work, Fracture Energy, and Fracture Toughness of Oil Well Cement Sheath using HCCD Test and CSTBD Test. Journal of Mining and Environment, 14(2), 619-634.

[16]. Mortezaie, R., Mohammadi, S. D., & Sarfarazi, V. (2022). Preparation of Heterogeneous Rock-Like Samples Containing Non-Persistent Notch; Different Layout. Journal of Mining and Environment, 13(4), 1139-1157.

[17]. Fu, J., Safaei, M. R., Haeri, H., Sarfarazi, V., Fatehi Marji, M., Xu, L., & Arefnia, A. (2022). Experimental investigation on deformation behavior of circular underground opening in hard soil using a 3D physical model. Journal of Mining and Environment, 13(3), 727-749.

[18]. Kumar, H., Deb, D., & Chakravarty, D. (2017). Design of crown pillar thickness using finite element method and multivariate regression analysis. International Journal of Mining Science and Technology, 27(6), 955-964.

[19]. Liu, J., Lu, P., & Zhang, L. (2022). Influence of section shape and buried depth on rock loosening zone around underground roadway in coal mine. ACS omega, 7(38), 34296-34308.

[20]. Fávero, L. P., Belfiore, P., & de Freitas Souza, R. (2023). Data science, analytics and machine learning with R. Academic Press.

[21]. Xiang‐Hui, Q. I. N., Peng, Z., Cheng‐Jun, F. E. N. G., Wei‐Feng, S. U. N., Cheng‐Xuan, T. A. N., Qun‐Ce, C. H. E. N., & You‐Ru, P. E. N. G. (2014). In‐situ stress measurements and slip stability of major faults in Beijing region, China. Chinese Journal of Geophysics, 57(4), 415-430.

[22]. Herget, G. (1987, February). Stress assumptions for underground excavations in the Canadian Shield. In International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts (Vol. 24, No. 1, pp. 95-97). Pergamon.

[23]. Zhao, X., & Zhou, X. (2022). Design method and application of stope structure parameters in deep metal mines based on an improved stability graph. Minerals, 13(1), 2.

[24]. Henning, J. G., & Mitri, H. S. (2007). Numerical modelling of ore dilution in blasthole stoping. International Journal of Rock Mechanics and Mining Sciences, 44(5), 692-703.

[25]. Yao, X., Allen, G., & Willett, M. (1999). Dilution evaluation using cavity monitoring system at HBMS—trout lake mine. In Proceeding of the 101st CIM annual general meeting, Calgary.

[26]. Diederichs, M. S. (2000). Instability of hard rockmasses, the role of tensile damage and relaxation.

[27]. Pérez-Rey, I., Moreno, J., & Muñiz-Menéndez, M. (2021, August). The role of joint spacing on the stability analysis of wedge failures. In IOP Conference Series: Earth and Environmental Science (Vol. 833, No. 1, p. 012095). IOP Publishing.

[28]. Moayedi, H., Mosallanezhad, M., Rashid, A. S. A., Jusoh, W. A. W., & Muazu, M. A. (2020). A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications. Neural Computing and Applications, 32, 495-518.

[29]. Koopialipoor, M., Fahimifar, A., Ghaleini, E. N., Momenzadeh, M., & Armaghani, D. J. (2020). Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance. Engineering with Computers, 36, 345-357.

[30]. Al-Shamisi, M. H., Assi, A. H., & Hejase, H. A. (2013). Artificial neural networks for predicting global solar radiation in Al Ain city-UAE. International journal of green energy, 10(5), 443-456.

[31]. Riedmiller, M. (1994). Advanced supervised learning in multi-layer perceptrons—from backpropagation to adaptive learning algorithms. Computer Standards & Interfaces, 16(3), 265-278.

[32]. Trivedi, R., Singh, T. N., Mudgal, K., & Gupta, N. (2014). Application of artificial neural network for blast performance evaluation. International Journal of Research in Engineering and Technology, 3(5), 564-574.

[33]. Wang, X., Lu, H., Wei, X., Wei, G., Behbahani, S. S., & Iseley, T. (2020). Application of artificial neural network in tunnel engineering: A systematic review. IEEE Access, 8, 119527-119543.

[34]. Nikafshan Rad, H., Bakhshayeshi, I., Wan Jusoh, W. A., Tahir, M. M., & Foong, L. K. (2020). Prediction of flyrock in mine blasting: a new computational intelligence approach. Natural Resources Research, 29, 609-623.

Numerical Analysis and Predictive Modeling Using Artificial Intelligence of the Relaxation Zone Around Hangingwall of Sublevel Open Stopes in Underground Mines

References

References

Volume 15, Issue 4October 2024Pages 1321-1342

Volume 15, Issue 4
October 2024
Pages 1321-1342