Document Type : Original Research Paper

Authors

1 Department of Mining Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 School of Geology, College of Science, University of Tehran, Tehran, Iran

3 Department of Mining Engineering, Faculty of Engineering, Tarbiat Modares University, Tehran, Iran

10.22044/jme.2021.10425.1993

Abstract

Mechanized tunneling in rocks is based on fracture propagation and rock fragmentation under disc cutters. Rock chipping is an efficient kind of fragmentation process, while the grinding process may occur under special conditions. The cutter-head penetration is an appropriate parameter involved in order to distinguish between the chipping and grinding processes in rock cutting. In this work, the grinding and chipping processes are investigated in the Uma-Oya water conveyance tunnel in Sri Lanka. The Uma-Oya project is a water transfer, hydropower, and irrigation system in the SE part of the central highland region of Sri-Lanka. From a geological viewpoint, most parts of the tunnel route in the studied section consist of very strong and abrasive metamorphic rocks that potentially are susceptible to grinding occurrence during the boring process under disc cutters. In this work, firstly, data processing is performed in order to identify the boundary between chipping and grinding. Then the chipping and grinding processes are modeled using the practical numerical and artificial intelligent methods. In the numerical modeling stage, we try to make the modeling as realistic as possible. The results obtained from these modeling methods show that for the penetrations less than 3 mm/rev, the grinding process is dominant, and for the penetrations more than 3 mm/rev, rock chipping occurs. Also, in the numerical modeling, no significant fracture expansion is observed in the rock when the penetration is less than 3 mm/rev. Moreover, it can be seen in the numerical modeling of the chipping process that the propagated fractures come together and the chips are created.

Keywords

[1]. Gong, Q., & Zhao, J. (2009). Development of a rock mass characteristics model for TBM penetration rate prediction. International journal of Rock mechanics and mining sciences, 46(1), 8-18.

[2]. Sapigni, M., Berti, M., Bethaz, E., Busillo, A., & Cardone, G. (2002). TBM performance estimation using rock mass classifications. International Journal of Rock Mechanics and Mining Sciences, 39(6), 771-788.

[3]. Rostami, J. (1997). Development of a force estimation model for rock fragmentation with disc cutters through theoretical modeling and physical measurement of crushed zone pressure (Doctoral dissertation, Colorado School of Mines).

[4]. Bruland, A. (1998). Hard rock tunnel boring: Drillability test methods. Project report 13A-98, NTNU Trondheim, 21.

[5]. Macias, F. J. (2016). Hard rock tunnel boring: performance predictions and cutter life assessments.

[6]. Barton, N. R. (2000). TBM tunnelling in jointed and faulted rock. Crc Press.

[7]. Yagiz, S. (2008). Utilizing rock mass properties for predicting TBM performance in hard rock condition. Tunnelling and Underground Space Technology, 23(3), 326-339.

[8]. Hassanpour, J., Rostami, J., & Zhao, J. (2011). A new hard rock TBM performance prediction model for project planning. Tunnelling and Underground Space Technology, 26(5), 595-603.

[9]. Nelson, P., O'Rourke, T. D., & Kulhawy, F. H. (1983, January). Factors affecting TBM penetration rates in sedimentary rocks. In The 24th US Symposium on Rock Mechanics (USRMS). American Rock Mechanics Association.

[10]. Gertsch, R., Gertsch, L., & Rostami, J. (2007). Disc cutting tests in Colorado Red Granite: Implications for TBM performance prediction. International Journal of rock mechanics and mining sciences, 44(2), 238-246.

[11]. Khorasani, E., Zare Naghadehi, M., Jimenez, R., Tarigh Azali, S., Jalali, S. M. E., & Zare, S. (2018). Performance analysis of tunnel-boring machine by probabilistic systems approach. Proceedings of the Institution of Civil Engineers-Geotechnical Engineering, 171(5), 422-438.

[12]. Saadati, M., Weddfelt, K., & Larsson, P. L. (2020). A Spherical Indentation Study on the Mechanical Response of Selected Rocks in the Range from Very Hard to Soft with Particular Interest to Drilling Application. Rock Mechanics and Rock Engineering, 53(12), 5809-5821.

[13]. Goodarzi, S., Hassanpour, J., Yagiz, S., & Rostami, J. (2021). Predicting TBM performance in soft sedimentary rocks, case study of Zagros mountains water tunnel projects. Tunnelling and Underground Space Technology, 109, 103705.

[14]. Xu, H., Geng, Q., Sun, Z., & Qi, Z. (2021). Full-scale granite cutting experiments using tunnel boring machine disc cutters at different free-face conditions. Tunnelling and Underground Space Technology, 108, 103719.

[15]. Gong, Q. M., Jiao, Y. Y., & Zhao, J. (2006). Numerical modelling of the effects of joint spacing on rock fragmentation by TBM cutters. Tunnelling and Underground Space Technology, 21(1), 46-55.

[16]. Liu, H. Y., Kou, S. Q., Lindqvist, P. A., & Tang, C. A. (2002). Numerical simulation of the rock fragmentation process induced by indenters. International Journal of Rock Mechanics and Mining Sciences, 39(4), 491-505.

[17]. Li, X. F., Li, H. B., Liu, Y. Q., Zhou, Q. C., & Xia, X. (2016). Numerical simulation of rock fragmentation mechanisms subject to wedge penetration for TBMs. Tunnelling and Underground Space Technology, 53, 96-108.

[18]. Gong, Q. M., Zhao, J., & Jiao, Y. Y. (2005). Numerical modeling of the effects of joint orientation on rock fragmentation by TBM cutters. Tunnelling and underground space technology, 20(2), 183-191.

[19]. Fang, Y., Yao, Z., Xu, W., Tian, Q., He, C., & Liu, S. (2021). The performance of TBM disc cutter in soft strata: A numerical simulation using the three-dimensional RBD-DEM coupled method. Engineering Failure Analysis, 119, 104996.

[20]. Sarfarazi, V., Haeri, H., Shemirani, A. B., Hedayat, A., & Hosseini, S. S. (2017). Investigation of ratio of TBM disc spacing to penetration depth in rocks with different tensile strengths using PFC2D. Computers and Concrete, 20(4), 429-437.

[21]. Adoko, A. C., Gokceoglu, C., & Yagiz, S. (2017). Bayesian prediction of TBM penetration rate in rock mass. Engineering Geology, 226, 245-256.

[22]. Acaroglu, O., Ozdemir, L., & Asbury, B. (2008). A fuzzy logic model to predict specific energy requirement for TBM performance prediction. Tunnelling and Underground Space Technology, 23(5), 600-608.

[23]. Ghasemi, E., Yagiz, S., & Ataei, M. (2014). Predicting penetration rate of hard rock tunnel boring machine using fuzzy logic. Bulletin of Engineering Geology and the Environment, 73(1), 23-35.

[24]. Benardos, A. G., & Kaliampakos, D. C. (2004). Modelling TBM performance with artificial neural networks. Tunnelling and Underground Space Technology, 19(6), 597-605.

[25]. Yagiz, S., & Karahan, H. (2015). Application of various optimization techniques and comparison of their performances for predicting TBM penetration rate in rock mass. International Journal of Rock Mechanics and Mining Sciences, 80, 308-315.

[26]. Yagiz, S., & Karahan, H. (2011). Prediction of hard rock TBM penetration rate using particle swarm optimization. International Journal of Rock Mechanics and Mining Sciences, 48(3), 427-433.

[27]. Harandizadeh, H., & Armaghani, D. J. (2021). Prediction of air-overpressure induced by blasting using an ANFIS-PNN model optimized by GA. Applied Soft Computing, 99, 106904.

[28]. Rostami, J., Ozdemir, L., & Nilson, B. (1996, May). Comparison between CSM and NTH hard rock TBM performance prediction models. In Proceedings of Annual Technical Meeting of the Institute of Shaft Drilling Technology, Las Vegas (pp. 1-10).

[29]. Farrokh, E., Rostami, J., & Laughton, C. (2012). Study of various models for estimation of penetration rate of hard rock TBMs. Tunnelling and Underground Space Technology, 30, 110-123.

[30]. Lin, L., Xia, Y., & Wu, D. (2020). A hybrid fuzzy multiple criteria decision-making approach for comprehensive performance evaluation of tunnel boring machine disc cutter. Computers & Industrial Engineering, 149, 106793.

[31]. Mohammadnejad, M., Dehkhoda, S., Fukuda, D., Liu, H., & Chan, A. (2020). GPGPU-parallelised hybrid finite-discrete element modelling of rock chipping and fragmentation process in mechanical cutting. Journal of Rock Mechanics and Geotechnical Engineering, 12(2), 310-325.

[32]. Villeneuve, M. C., Diederichs, M. S., & Kaiser, P. K. (2012). Effects of grain scale heterogeneity on rock strength and the chipping process. International Journal of Geomechanics, 12(6), 632-647.

[33]. Villeneuve, M. C. (2017). Hard rock tunnel boring machine penetration test as an indicator of chipping process efficiency. Journal of Rock Mechanics and Geotechnical Engineering, 9(4), 611-622.

[34]. Hassanpour, J., Firouzei, Y., & Hajipour, G. (2021). Actual performance analysis of a double shield TBM through sedimentary and low to medium grade metamorphic rocks of Ghomrood water conveyance tunnel project (lots 3 and 4). Bulletin of Engineering Geology and the Environment, 80(2), 1419-1432.

[35]. FARAB, “ENGINNERING GEOLOGY Headrace Tunnel,” Tehran, Iran, 2012.

[36]. Gehring, K. (2009). The influence of TBM design and machine features on performance and tool wear in rock. Geomechanics and Tunnelling, 2(2), 140-155.

[37]. Gong, Q. M., & Zhao, J. (2007). Influence of rock brittleness on TBM penetration rate in Singapore granite. Tunnelling and underground space technology, 22(3), 317-324.

[38]. Lak, M., Fatehi Marji, M., Yarahamdi Bafghi, A. R., & Abdollahipour, A. (2019). Discrete element modeling of explosion-induced fracture extension in jointed rock masses. Journal of Mining and Environment, 10(1), 125-138.

[39]. Cundall, P. A., & Hart, R. D. (1993). Numerical modeling of discontinua. In Analysis and design methods (pp. 231-243). Pergamon.

[40]. Lak, M., Baghbanan, A., & Hashemolhoseini, H. (2017). Effect of seismic waves on the hydro-mechanical properties of fractured rock masses. Earthquake Engineering and Engineering Vibration, 16(3), 525-536.

[41]. Mohammadi, H., Farsangi, M. E., Rahmannejad, R., & Poor, H. N. (2007). TBM Advance Rate Prediction: An Artificial Neural Network Approach. In Third National Congress in Civil Engineering.