توسعه شاخص حفرپذیری توده سنگ درزه دار بر اساس شاخص شکست سنگ در حفاری با TBM
چکیده
پیش بینی حفرپذیری توده سنگ درزهدار در حفاری با ماشینهای حفار تمام مقطع تونل یکی از مسائل مهم در ارزیابی فنی و اقتصادی یک پروژه تونل سازی میباشد. با توجه به تأثیر پارامترهای مختلف در حفرپذیری توده سنگ، تا کنون مدلهای مختلفی برای پیشبینی این موضوع ارائه گردیده است. هدف این تحقیق ارائه مدلی جدید بر اساس توسعه شاخص شکست سنگ برای توده سنگ درزهدار به منظور پیشبینی حفرپذیری آن میباشد. برای این هدف در ابتدا بانک اطلاعاتی از پارامترهای ژئومکانیکی ، مشخصات هندسی درزهها (فاصله داری و زاویه) و عملیاتی ماشین حفار دو پروژه تونل انتقال آب کوئینز در آمریکا و تونل بلند زاگرس (قطعه 2) در ایران تشکیل گردیده است. سپس فاکتور خردایش کل (ارائه شده توسط برولند در سال 1979) محاسبه و با استفاده از نرم افرار SPSS و روش های رگرسیون غیرخطی شاخص شکست بدست آماده در آزمایشگاه برای توده سنگ درزهدار توسعه داده شده است. در ادامه نیز شاخص حفرپذیری بر اساس شاخص شکست توده سنگ درزه دار توسعه داده که ضریب همبستگی این شاخص 8/0 می باشد. مدل جدید در قطعه شمالی تونل انتقال آب کرمان مورد ارزیابی قرار گرفته که نتایج بیانگر مطابقت خوبی بین حفرپذیری پیش بینی شده با حفرپذیری واقعی دارد.
مراجع
Fatemi, S. A., & Ahmadi, M., & Rostami, J. (2016). Evaluation of TBM performance prediction models and sensitivity analysis of input parameters. Bulletin of Engineering Geology and the Environment DOI 10.1007/s10064-016-0967-2.
Rostami, J., & Ozdemir, L. (1993). A new model for performance prediction of hard rock TBMs. Proceedings of rapid excavation and tunneling conference, Boston, 13–17 June, p. 793–809.
Rostami, J., & Ozdemir, L., & Nilsen, B. (1996). Methods for predicting mechanical excavation performance and costs. Proceedings of annual technical meeting of the institute of shaft drilling technology, lasvegas, 1–3 May.
Bruland, A. (1998). Hard Rock Tunnel Boring. Doctoral Thesis., NorwegianUniversity of Science and Technology.
Bruland, A., & Dahlo, T., & Nilsen, B. (1995). Tunneling Performance Estimation Based on Drillability Testing. Proceedings of 8 th International Congress of Rock Mechanics, Sep. 25-30, Tokyo, Japan.
Yagiz, S. (2002). Dewelopment of rock fracture and brittleness indices to Quantify the effects of rock mass features and toughnessn the CMS model basic penetration for hardrock tunnel machines. Thesis Doctor of Philosophy, CSM.
Yagiz, S. (2008). Utilizing rock mass properties for predicting TBM performance in hard rock condition. Tunnelingl Underground Space Technology, 23(3),326–39.
Barton, N. (2000). TBM Tunnelling in Jointed and Faulted Rock. Balkema, Rotterdam.
Ramezanzadeh, A., & Rostami, J., & Richard, K. (2005). Influence of rock mass properties on performance of hard rock TBMS. Retc Proceedigs, Chapter 56.
Ramezanzadeh, A., & Rostami, J., & Tadic, D. (2008). Impact of Rock Mass Characteristics on Hard Rock Tunnel Boring Machine Performance. 13th Australian Tunneling Conference, Melbourne, VIC, 4 - 7 May.
Bieniawski, Z.T., & Celada, B., & Galera, J. M., & Alvarez, M. (2006). Rock masse excavability indicator: new way to selecting the optimum tunnel construction method. Tunnelling Underground Space Technolgy, 21, 237, 3–4.
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.
Khademi, J., & Shahriar, K., & Rezai, B., & Rostami, J. (2010). Performance prediction of hard rock TBM using Rock Mass Rating (RMR) system. Tunnelling and Underground Space Technology, 25, 333–345.
Hassanpour, J. (2010). Analysis of actual TBM performance in Ghomrood project. Bulletin of Iranian Tunneling Association. 9, (article in Persian).
Hassanpour, J., &Rostami, J., & Zhao, J. (2011) A new hard rock TBM performance prediction model for project planning. Tunnelling and Underground Space Technology, 26, 595–603.
Farrokh, E. (2012). Study of utilization feactor and advance rate of hard rock TBMS. Doctor of Philosophy, Pennsylvania State University.
Zare Naghadehi, M., & Ramezanzadeh, A. (2016). Models for estimation of TBM performance in granitic and mica gneiss hard rocks in a hydropower tunnel. Bulletin of Engineering Geology and the Environment.
Wilfing, L. (2016). The Influence of Geotechnical Parameters on Penetration Prediction in TBM Tunnelingin Hard Rock. Tehnische chnische university MÜNCHEN.
Macias, F. (2016). Hard Rock Tunnel Boring Performance Predictions and Cutter Life Assessments. Thesis for the degree of Philosophiae Doctor, Norwegian University of Science and Technology.
Maleki, M. R. (2018). Rock Joint Rate (RJR); a new method for performance prediction of tunnel boring machines (TBMs) in hard rocks. Tunnelling and Underground Space Technology, 73, 261-286.
Yin, L.J., & Gong, Q.M., & Zhao, J. (2014) Study on rock mass boreability by TBM penetration test under different in situ stress conditions. Tunnelling and Underground Space Technology, 43, 413-425.
Hamilton, W., & Dollinger, G. (1979). Optimizing tunnel boring machine and cutter design for greater boreability. RETC Proc Atlanta, 1, 280–296.
Gong, Q.M., & Zhao, J., & Jiao. Y.Y. (2005). Numerical modelling of the effects of joint orientation on rock fragmentation by TBM cutters. Tunnel Underground Space Technology, 20(2),183–91.
Delisio, A., & Zhao, J., & Einstein, H. (2013). Analysis and prediction of TBM performance in blocky rock conditions at the Lötschberg Base Tunnel. Tunnelling and Underground SpaceTechnology, (33), 131-142.
Salimi, A., & Moormann, C., & Singh, T., & Jain, P. (2015). TBM performance prediction in rock tunneling using various artificial intelligence algorithms. 11th Iranian and 2nd Regional Tunnelling Conference "Tunnels and the Future".
Mohammadi, S., & Torabi Kaveh, M., & Bayati, M. (2014). Prediction of TBM penetration rate using intact and mass rock properties (case study: Zagros long tunnel, Iran). Arabian Journal of Geosciences, DOI 10.1007/s12517-014-1465-0.
Yagiz, S. (2017). New equations for predicting the field penetration index of tunnel boring machines in fractured rock mass. Arab J Geosci, 10,33, DOI 10.1007/s12517-016-2811-1.
Balci, C. (2009). Correlation of rock cutting tests with field performance of a TBM in a highly fractured rock formation: A case study in Kozyatagi-Kadikoy Metro Tunnel,
Yagiz, S. (2015). The Punch Penetration Test for Estimating Machine Performance. 33rd International Conference and Exhibition. İstanbul.
Meng, F., & Zhou, H., & Zhang, Ch. (2015). Evaluation Methodology of Brittleness of Rock Based on Post-Peak Stress-Strain Curves. Journal of Rock Mechanic and Rock Engineering, 48, 1787-1805.
Yagiz, S. (2006). A model for the prediction of tunnel boring machine performance. Geological Society of London, Paper number 383.
Paltrinieri, E. (2015). Analysis of TBM tunnelling performance in faulted and highly fractured rocks. Doctor of philosophy thesis.
Yagiz, S., & Yazitova, A. (2015) Using Intact Rock Brittleness for Assessing TBM Penetration. Proceedings of the 4th World Congress on Civil, Structural, and Environmental Engineering (CSEE’19), Paper No. ICGRE 122.
Gong, Q. M., & Zhao, Q.M., & Jiang, J. (2007). In situ TBM penetration tests and rock mass bore ability analysis in hard rock tunnels. Tunnelling and Underground Space Technology, 3, 303–316.
Kahraman, S. (2002). Correlation of TBM and drilling machine performance with rock brittlenecss. Journal of engineering geology, (65)4, 269-283.
Jeong, H.Y., & Woo.Cho, J., & Jeon, S., & Rostami, J. (2015). Performance Assessment of Hard Rock TBM and Rock Boreability Using Punch Penetration Test. Rock Mechanics and Rock Engineering, DOI 10.1007/s00603-015-0834-7.
Bilgin, N., & Copur, H., & Balci, C. (2016). TBM Excavation in Difficult Ground Conditions Case Studies from Turkey. 1rdEdition, Istanbul Technical University Faculty of Mines, Mining Engineering Department.
SOI Company. (2016). Engineering geological report of Kerman water conveyance tunnel. unpublished report.