تعداد نشریات | 161 |
تعداد شمارهها | 6,532 |
تعداد مقالات | 70,501 |
تعداد مشاهده مقاله | 124,114,062 |
تعداد دریافت فایل اصل مقاله | 97,217,827 |
Assessment of Double Shield TBM performance by using downtime index (DTI) | ||
Geopersia | ||
مقاله 1، دوره 13، شماره 1 - شماره پیاپی 22287825، فروردین 2023، صفحه 1-14 اصل مقاله (2.25 M) | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22059/geope.2022.304879.648636 | ||
نویسندگان | ||
Majid Tajik؛ Mashalah Khamechiyan* | ||
Department of Engineering Geology, Tarbiat Modares University, Tehran, Iran | ||
چکیده | ||
In mechanized tunneling, TBM performance prediction is vital to estimate the time and cost of the project. Therefore, calculating the performance parameters is so important. The utilization coefficient depends on management parameters, personal ability, logistic utility and equipment, tunnel characteristics, objectives and geological conditions. Although in each of the main models same as CSM, NTNU and QTBM, the specific parameters used to estimate the utilization coefficient, the effect of management factor and interactions and overlapping factors not considered. On the other hand, many parameters have a severe dependence on each other and may simultaneously affect the performance of the TBM. Therefore, the interaction matrix can be used to evaluate the interaction of parameters on each other and on TBM performance. The effect of 18 parameters on the utilization coefficient was evaluated by the matrix method in Karaj water conveyance tunnel. The interactions of these parameters show that the lack of utility services and shift change have the most significant impact on TBM performance. By recording the actual delays in each parts of tunnel, the downtime index (DTI) is obtained; this index has a direct relationship with tunnel boring time and is inversely related to TBM performance | ||
کلیدواژهها | ||
TBM performance؛ interaction matrices؛ utilization coefficient؛ downtime index؛ Karaj | ||
عنوان مقاله [English] | ||
- | ||
مراجع | ||
Abd Al-Jalil, Y. Q., 1998. Analysis of Performance of Tunnel Boring Machine-Based System. PhD Thesis, the University of Texas. Adesida, Patrick A., 2022. Powder factor prediction in blasting operation using rock geo-mechanical properties and geometric parameters. International Journal of Mining and Geo-Engineering, 56(1): 25-32. Alsahly, A., Hegemann, F., Ko¨ nig, M., Meschke, G., 2020. Integrated BIM-to-FEM approach in mechanised tunnelling. Geomechanics and Tunnelling, 13(2): 212-220. Azadmehr, A, Jalali, S.M., Pourrahimian, Y., 2019. An application of rock engineering system for assessment of the rock mass fragmentation: a hybrid approach and case study. Rock Mechanics and Rock Engineering, 52(11): 4403-4419. Barton, N., 1999. TBM Performance Estimation in Rock Using QTBM. Tunnel & Tunneling International, 31(90): 30-34. Barton, N., 2000. TBM Tunneling in Jointed and Faulted Rock. Balkema. Rotterdam. Benardos, A.G., Kaliampakos, D.C., 2004. Modelling TBM performance with artificial neural networks. Tunnelling and Underground Space Technology, 19(6): 597-605. Bieniawski, Z.T., Celada, B., Galera, J.M., 2007. Predicting TBM Excavability. Tunnel & Tunnelling International, 25-28. Bruland, A., 1998. Advance Rate and Cutter Wear, Hard Rock Tunnel Boring Machine. PhD Thesis, Trondheim Norwegian University of Science and Technology, NTNU 3. Ceryan, N., Ceryan, S., 2008. An application of the interaction matrices method for slope, failure susceptibility zoning: Dogankent settlement area (Giresun, NE Turkey). Bulletin of Engineering Geology and the Environment, 67 (3): 375-385. Farmer, I.W., Glossop, N.H., 1980. Mechanics of disc cutter penetration. Tunnels and Tunneling, 12 (6): 22-25. Farrokh. E., 2012. Study of Utilization Factor and Advance Rate of Hard Rock TBMs. PhD Thesis, Department of Energy and Minerals Engineering, Pennsylvania State University. Fattahi, H. 2018. An estimation of required rotational torque to operate horizontal directional drilling using rock engineering systems. Journal of Petroleum Science and Technology, 8 (1): 83-97. Freitag, S., Cao, B.T., Ninic, J., Meschke, G., 2018. Recurrent neural networks and proper orthogonal decomposition with interval data for real-time predictions of mechanised tunnelling processes. Computers & Structures, 207: 258-273. Frough, O., Khetwal, A., Rostami, J., 2019. Predicting TBM utilization factor using discrete event simulation models. Tunnelling and Underground Space Technology, 87: 91-99. Geopersia 2023, 13(1): 1-14 13 Frough, O., Torabi, S.R., Yagiz, S., Tajik, M., 2012. Effect of Rockmass Conditions on TBM Utilization Factor in Karaj - Tehran Water conveyance tunnel. World Tunneling Congress, Thailand. Frough, O., Torabi, S.R., Ramezanzadeh, A., Yagiz, S., 2011. Influence of rock mass conditions on TBM downtime in Karaj water conveyance tunnel. First Asian and 9th Iranian tunnel symposium (Persian). Gansser, A., Huber, H., 1962. Geological Observation in the Central Elburz, Iran. Schweizerische Mineralogische Und Petrographische Mitteilungen 42. Gong, Q., Jiao, Y., Zhao, J., 2006. Numerical modeling of the effects of joint spacing on rock fragmentation by TBM cutters. Tunnelling and Underground Space Technology, 21(1):46-55. Gong, Q.M., Zhao, J., 2009. Development of a rock mass characteristics model for TBM penetration rate prediction. International Journal of Rock Mechanics & Mining Sciences, 46 (1): 8-18. Goodarzi, A., 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. Graham, P.C., 1976. Rock exploration for machine manufactures, Proceedings Symposium on Exploration for Rock Engineering, Johannesburg. Hashemnejad, A., Fatemi Aghda, S.M., Talkhablou, M., 2020. Mechanized tunnelling in hydrothermally altered grounds: The effect of hydrothermal fluids on the rock behaviour in the central Iran. Tunnelling and Underground Space Technology, ID: 216303248. 10.1016/j.tust.2020.103340 Hassanpour, J., Rostami, J., Khamhechiyan, M., Tavakoli, H.R., 2009. "TBM Performance Analysis in Pyroclastic Rocks: Case History of Karaj Water Conveyance Tunnel. Rock Mechanics and Rock Engineering Journal, 43:427-445. Hudson, J.A. 1992. Rock Engineering Systems, Theory and Practice. Ellis Horwood, Chichester. Hudson, J.A., Harrison, J.P., 1997. Engineering rock mechanics an introduction to the principles. Pergamon. Innaurato, N., Mancini, R., Rondena, E., Zaninetti, A., 1991. Forecasting and effective TBM performances in a rapid excavation of a tunnel in Italy. Proceeding of 7th International Congress on Rock Mechanics, 1009-1014. KhaloKakaie, R., Zare naghadehi, M., 2009. The analysis and classification of rock slopes instability potential in Khosh - Yeylagh mountainous road using systems approach (In Persian). Journal of Iranian Association of Engineering Geology, 2 (1,2): 19-32. KhaloKakaie, R., Zare naghadehi, M., 2012. The assessment of rock slope instability along the Khosh- Yeylagh Main Road (Iran) using a systems approach. Environmental Earth Sciences, 67 (3): 665-682. Mahmoodzadeh, A., Mohammadi, M., Daraei, A., Ali, H.F.H., AlSalihi, N.K., Omer, R.M.D., 2020. Forecasting maximum surface settlement caused by urban tunneling. Automation in Construction, 120: 103375. Moosazadeh, S., Aghababaie, H., Hoseinie, S.H., Ghodrati,B., 2018. Simulation of tunnel boring machine utilization: A case study. Journal of Mining & Environment, 9 (1): 53-60. Nelson, P., O’Rouke, T.D., Kulhawy, F.H., 1983. Factors affecting TBM penetration rates in sedimentary rocks. 24th U.S. Symposium on Rock Mechanics. Ninic, J, Bui, H.G., Meschke, G., 2020. BIM-to-IGA: A fullyautomatic design-through-analysis workflow for segmented tunnel linings. Advanced Engineering Informatics, 46: 101137. Ninic, J., Freitag, S., Meschke, G., 2017. A hybrid finite element and surrogate modelling approach for simulation and monitoring supported TBM steering. Tunnelling and Underground Space Technology, 63: 12-28. Palmstrom, A. 1995. RMi Parameters Applied In Prediction of Tunnel Boring Penetration. In: A. Palmtrom, RMi - A Rock Mass Characterization System for Rock Engineering Purposes (Chapter7), PhD Thesis, Oslo University, Norway. Ramezanzadeh, A., Rostami, J., Kastner, R., 2002. Performance Prediction Models for Hard Rock Tunnel Boring Machines. The 6th Iranian Tunneling Conference . Ramezanzadeh, A., 2005. Performance analysis and development of new models for performance prediction of hard rock TBMs in rock mass. PhD Thesis, Lyon: Institute National des Sciences Appliqu´ees. Rostami, J, Ozdemir, L., 1993. A new model for performance prediction of hard rock TBMs. RETC conference proceedings, Boston. Rostami, J., Ozdemir, L., Nilson, B., 1997. Comparison between CSM and NTH Hard Rock TBM 14 Tajik & Khamehchiyan Performance Prediction models. Colorado School of Mines, Golden, Colorado, USA. Roxborough, F.F., Phillips, H.R., 1975. Rock excavation by disc cutter. International Journal of Rock Mechanics and Mining Sciences, 12(12): 361-366. Rozos, D., Pyrgiotis, L., Skias, S., Tsagaratos, P., 2008. An implementation of rock engineering system for ranking the instability potential of natural slopes in Greek territory. An application in Karditsa County. Landslides, 5: 261-270. Sadeghi, M., Rasouli, V., 2011. application of rock engineering systems in evaluation of stability of underground excavations. Amirkabir Journal of Civil Engineering, 43(1): 89- 95. SAHEL consultant engineers institute, 2011. Engineering Geology As built Maps and Site Reports of Conveyance Tunnel (Lot 2). SCE Archive. SAHEL consultant engineers institute, 2009. Engineering Geology Report of Karaj-Tehran Water Conveyance Tunnel (Lot 2). unpublished report. Sanio, H.P., 1985. Prediction of the Performance of Disc Cutters in Anisotropic Rock. International Journal of Rock Mechanics and Mining Sciences & Geomechanics, 22(3): 153-161. Sapigni, M., Berti, M., Bethaz, E., Busillo, A., Cardone, G., 2002. TBM performance estimation Using Rock mass Classification. International Journal of Rock Mechanic and Mining Sciences, 39(6):771- 788. Sato, K., Gong, F., Itakura, K,. 1991. Prediction of Disc Cutter Performance using a Circular Rock Cutting Ring. 1st International Mine Mechanization and Automation Symposium. Simoes, M.G., Kim., T., 2006. Fuzzy Modeling Approaches for the Prediction of Machine Utilization in Hard Rock Tunnel Boring Machines. Conference Record of the 2006 IEEE Industry Applications Conference Forty-First IAS Annual Meeting, 2: 947-954. Snowdon, A.R.D., Ryley, M., Temporal, J., 1982. Study of Disc Cutting in Selected British Rocks. International Journal of Rock Mechanics and Mining Science & Geomechanics, ID: 140586186. Tajik, M., Oruji, M., Novin, A., 2010. Assessment of TBM performance in tunnel excavation, first lot of Karaj - Tehran water conveyance tunnel . Journal of Iranian Association of Engineering Geology, 3(1, 2): 41-50. Tarkoy, P.J. 1975. Rock hardness index properties and geotechnical parameters for predicting tunnel boring machine performance. PhD Thesis, University of Illinois at Urbana-Champaign. Xiao, H., Yang, W., Hua, J., Zhang, Y., Jing, L., 2022. Significance and methodology: Preprocessing the big data for machine learning on TBM performance. Underground Space, 7(4): 680-701. Xu, C., Liu, X.L., Wang, E.Z., Wang, S.J., 2021. Prediction of tunnel boring machine operating parameters using various machine learning algorithms. Tunnelling and Underground Space Technology, 109: 103699. Yaghoubi, 2010. prediction of TBM performance by rock engineering systems method. Thesis for M.Sc. Degree, University of Kerman (Persian). Yagiz, S. 2008. Utilizing rock mass properties for predicting TBM performance in hard rock condition. Tunnelling and Underground Space Technology, 23(3):326-339. Yavari, F., Mansoori, H., Ebrahimi, M.A., 2011. Determination of TBM advance rate by rock engineering systems. First Asian and 9th Iranian tunnel symposium (Persian). Zare Naghadehia, M., Samaei, M., Ranjbarnia, M., Nourani, V., 2018. State-of-the-art predictive modeling of TBM performance in changing geological conditions through gene expression programming. Measurement, 126: 46-57. Zhang, L.Q., Yang, Z.F., Liao, Q.L., Chen, J., 2004. An application of the rock engineering system (RES) methodology to rockfall hazard assessment on the Chengdu-Lhasa Highway, China. International Journal of Rock Mechanics & Mining Sciences, 41(3): 526-527. | ||
آمار تعداد مشاهده مقاله: 392 تعداد دریافت فایل اصل مقاله: 1,203 |