Geomechanical characterization of rock mass rating and numerical modeling for underground mining excavation design
Keywords:
Geomechanics, Characterization, Mining, RMR, FEM, Underground excavation, Tunnel
Abstract
The objective of the study is the geomechanical characterization of the rock mass rating RMR system and numerical modeling for mining underground excavation design of the Djebel El Ouahch tunnel, in Constantine (Algeria).The geological and geotechnical character- ization of the rock mass is important for the design of underground mining excavations. In this article, we present the results of the RMR characterization of the rock mass and the numerical modeling by the finite element method (FEM), under the conditions of the Djebel El Ouahch tunnel, Constantine (Algeria).The RMR system is a useful tool for characterization of the rock mass quality and establishing the appropriate support system. For poor rock (Class IV), the excavation should be top heading and bench 1.0 m – 1.5 m advance in top heading. Support should be installed concurrently with excavation, 10 m from face. Rock bolting should be systematic with 4 m – 5 m long, spaced 1.5 m – 1.5 m in the crown and walls with wire mesh, Shotcrete of 100 m -150 mm in the crown and 100 mm in sides. The steel sets should be light to medium ribs spaced 1.5 m only when required. The rock mass consists of generally poor rocks with average stand- up time of 10 hours for 2.5m span with mass cohesion ranges between 100 kPa – 200 kPa and rock mass friction angle ranges from 15° to 35°. The FEM project due to its precision calculates the safety factor and evaluates the principal deformations and displacements of the rocks mass .The originality of this work lies in the use of two different approaches , the RMR system and numerical method (FEM) for analyzing the quality and evaluation of the deformations and displace- ments of rock mass .This method has become a very common practice in underground mining excavation design.This study illustrates that the results obtained by RMR of the argillite rock mass in the case is 28.00 ,ranging from 21.0 to 40.0 classified as Class IV (Poor Rock), while the results of FEM reveal that in accordance with the poor quality of the rocks, large deformations and displacements were observed around the underground mining excavation, which can be at the origin of the ruptures. The value of the safety factor of the order of 0.95 to 1.24 shows the instability of the excavation, and the appearance of very considerable hazard zones in the argillite layer.References
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19. Soufi, A., Bahi, L., Ouadif, L., Kissai, D.E. (2018). Correlation between Rock mass rating, Q-system and Rock mass index based on field data. MATEC Web of Conferences 149, 02030. https://doi.org/10.1051/ matecconf/201814902030
20. Wesołowski, M. (2016). Numerical modeling of exploitation relics and faults influence on rock mass deforma- tions. Archives of Mining Sciences. Vol. 61, 893-906. DOI 10.1515/amsc-2016-0059
21. Zhang, L. (2017). Evaluation of rock mass deformability using empirical methods – A review. Underground Space, 2(1), 1-15. https://dx.doi.org/10.1016/j. undsp.2017.03.003 2467-9674/2017
22. Zhang, Q., Huang, X., Zhu, H., and Li, J. (2019). Quantitative assessments of the correlations between rock mass rating (RMR) and geological strength index (GSI). Tun- nelling and Underground SpaceTechnology, 83, 73-81. https://doi.org/10.1016/j.tust.2018.09.015
2. Barton, N., Bieniawski, Z. (2008). RMR and Q-Setting Record Straight; Tunnels &Tunneling International Pro- gressive Media Markets Ltd.: London, UK, 2008.
3. Berkane, A., Karech, T. (2018). Numerical modeling of the pathological case of a damaged tunnel Application to Djebel El-Ouahch tunnel (east–west highway). Asian Journal of CivilEngineering, 19 (8), 913-925. https:// doi.org/10.1007/s42107-018-0072-x
4. Bieniawski, Z.T. (1989). Engineering Rock Mass Classifications, A Complete Manual for Engineers and Geologists in Mining, Civil, and Petroleum Engineering. John Wiley& Sons: Hoboken, NJ, USA.
5. Jiang, H. (2018). Simple three-dimensional Mohr-Coulomb criteria for intact rocks. International Journal of Rock Mechanics and Mining Sciences, 105, pp.145- 159. https://doi.org/10.1016/j.ijrmms.2018.01.036
6. Kim, J., Rehman, H., Ali, W., Naji, A. M. And Yoo, H. (2019). Weightage effect during back-calculation of rock-mass quality from the installed tunnel support in rock-mass rating and tunneling quality index system. Applied Sciences, 9 (10), 2065. doi:10.3390/ app9102065. www.mdpi.com/journal/applsci
7. Magomedov, I.A., and Sebaeva, Z. S. (2020). Comparative study of finite element analysis software packages. ICMSIT Journal of Physics: Conference Series 1515. doi:10.1088/17426596/1515/3/032073
8. Nejati, H. R., Ghazvinian, A., Moosavi, S. A., and Sarfarazi, V. (2014). On the use of the RMR system for estimation of rock mass deformation modulus. Bulletin of Engineering Geology and the Environment, 73 (2), 531-540. DOI 10.1007/s10064-013-0522-3
9. Pantaweesak,P.,Sontamino,P.,and Tonnayopas, D. (2019). Alternative Software for Evaluating Preliminary Rock Stability of Tunnel using Rock Mass Rating (RMR) and Rock Mass Quality.AndroidSmartphone. Engineering Journal, 23 (1), 95-108. https://www.engj.org/ DOI:10.4186/ej.2019.23.1.95
10. Pells, P. J., Bieniawski, Z. T., Hencher, S. R., and Pells, S. E. (2017). Rock quality designation (RQD): time to rest in peace. Canadian Geotechnical Journal, 54(6), 825-834. dx.doi.org/10.1139/cgj-2016-0012
11. Polemis, K., Silva, FCD., and Lima-Filho, FP., 2021. Estimating the rock mass deformation modulus: A comparative study of empirical methods based on 48 rock mass scenarios.Int. Eng. J., Ouro Preto, 74 (1), 39-49. https:// dx.doi.org/10.1590/037044672019740150
12. Rehman, Z. U., Hussain, S., Tahir, M., Sherin, S., Moham- mad, N., Dasti, N., Raza, S., & Salman, M. (2022). Numerical modelling for geotechnical assessment of rock mass behaviour and performance of support system for diversion tunnels using optimized Hoek-Brown parameters. Mining of Mineral Deposits, 16(1), 1–8. https://doi.org/10.33271/mining16.01.001
13. Rahmati, A., Faramarzi, L., Sanei, M. (2014). Development of a new method for RMR and Q classification method to optimize support system in tunneling. Frontiers of Structural and Civil Engineering. DOI 10.1007/ s11709-014-0262-x
14. Rehman, H., Naji, A.M., Kim, J-j,. Yoo, H.-K. (2018). Empirical Evaluation of Rock Mass Rating and Tun- neling Quality Index System for Tunnel Support Design. Appl. Sci.8, 782. doi:10.3390/app8050782. www. mdpi.com/journal/applsci
15. Roslee, R., Pirah, J. A., Zikiri, M. F., and Madri, A. N. (2020). Applicability Of The Rock Mass Rating (RMR) System For the Trusmadi Formation At Sabah, Malay- sia. Malaysian Journal of Geosciences MJG), 4(2), 96- 102. https://doi.org/10.26480/mjg.02.2020.96.102
16. Sadeghi, S., Teshnizi, E. S., and Ghoreishi, B. (2020). Correlations between various rock mass Classification /characterization systems for the Zagros tunnel-W Iran. Journal of Mountain Science, 17(7), 1790-1806. https://doi.org/10.1007/s11629-019-5665-7
17. Sean, N. W., Kallu, R. R., and Barnard, C. K. (2016). Correlation of the Rock Mass Rating (RMR) System with the Unified Soil Classification System (USCS): Introduction of the Weak Rock Mass Rating System (W-RMR). Rock Mech Rock Eng (2016) 49, 4507- 4518. DOI 10. 1007 /s00603-016-1090-1
18. Somodi, G., Bar, N., Kovács, L., Arrieta, M., Török, Á.,and Vásárhelyi, B. (2021). Study of Rock Mass Rating (RMR) and Geological Strength Index (GSI) Correla- tions in Granite, Siltstone, Sandstone and Quartzite Rock Masses. Applied Sciences, 11(8), 3351. https:// doi.org/10.3390/app11083351
19. Soufi, A., Bahi, L., Ouadif, L., Kissai, D.E. (2018). Correlation between Rock mass rating, Q-system and Rock mass index based on field data. MATEC Web of Conferences 149, 02030. https://doi.org/10.1051/ matecconf/201814902030
20. Wesołowski, M. (2016). Numerical modeling of exploitation relics and faults influence on rock mass deforma- tions. Archives of Mining Sciences. Vol. 61, 893-906. DOI 10.1515/amsc-2016-0059
21. Zhang, L. (2017). Evaluation of rock mass deformability using empirical methods – A review. Underground Space, 2(1), 1-15. https://dx.doi.org/10.1016/j. undsp.2017.03.003 2467-9674/2017
22. Zhang, Q., Huang, X., Zhu, H., and Li, J. (2019). Quantitative assessments of the correlations between rock mass rating (RMR) and geological strength index (GSI). Tun- nelling and Underground SpaceTechnology, 83, 73-81. https://doi.org/10.1016/j.tust.2018.09.015
Published
2023-04-09
How to Cite
Kimour, M., Boukelloul, M., Hafsaoui, A., Narsis, S., Benghadab, K., & Benselhoub, A. (2023). Geomechanical characterization of rock mass rating and numerical modeling for underground mining excavation design. Journal of Geology, Geography and Geoecology, 32(1), 67-78. https://doi.org/https://doi.org/10.15421/112308
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