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آنالیز حساسیت جامع در پیش بینی نشست سطحی ناشی از فرآیند تونل‌سازی مکانیزه | ||
| نشریه مهندسی عمران امیرکبیر | ||
| مقاله 15، دوره 53، شماره 6، شهریور 1400، صفحه 2497-2514 اصل مقاله (2.08 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2020.17432.6558 | ||
| نویسندگان | ||
| لیلا نیک اختر1؛ شکراله زارع* 2؛ حسین میرزائی نصیرآباد3 | ||
| 1دانشگاه صنعتی شاهرود- دانشکده نفت، ژئوفیزیک و معدن | ||
| 2دانشیار گروه مکانیک سنگ دانشگاه صنعتی شاهرود | ||
| 3دانشگاه صنعتی سهند | ||
| چکیده | ||
| آنالیز حساسیت جامع یک ابزار مفید و کاربردی برای شناسایی عدم قطعیت پارامترهای ورودی می‌باشد که به طور گسترده در علوم مختلف از جمله شبیه سازی مهندسی مورد استفاده قرار می‌گیرد. آنالیز حساسیت یکی از مراحل اساسی ساخت متا مدل یا مدل جایگزین است که با شناسایی پارامترهای موثر در امر تونل‌سازی باعث کاهش زمان و میزان محاسبات لازم می‌شود. در این تحقیق آنالیز حساسیت بر روی پارامترهای ژئوتکنیکی و عملیاتی ساخت تونل با ماشین حفاری مکانیزه از نوع تعادلی فشار زمین در خاک انجام شده است. به همین منظور فرآیند حفاری تونل با استفاده از روش تفاضل محدود در نرم افزار FLAC 3D به صورت سه بعدی مدل شد و مدل عددی ساخته شده با استفاده از داده مانیتورینگ حاصل از مسیر شرقی- غربی خط 7 مترو تهران اعتبارسنجی گردید. سپس با استفاده از روش تاثیر مقدماتی Morris، آنالیز حساسیت بر روی 6 پارامتر ورودی انجام و 3 پارامتر فشار سینه‌کار، وزن مخصوص و چسبندگی لایه‌ی خاکی که تونل در آن حفر شده است، به عنوان پارامترهای موثر و حساس در نتیجه نهایی شبیه سازی که در این تحقیق نشست سطحی حداکثر می‌باشد، انتخاب شد. در ادامه تعداد 100 عدد نمونه تصادفی برای پارامترهای موثر با استفاده از روش فرامکعب لاتین تولید و بعد از شبیه سازی عددی برای آن‌ها، با استفاده از شبکه عصبی مصنوعی نشست سطحی حداکثر پیش بینی شد. نتایج پیش بینی با متا مدل شبکه عصبی مصنوعی و مدل عددی ساخته شده با داده در فاز طراحی حدود 98% تطابق نشان دادند. | ||
| کلیدواژه‌ها | ||
| متا مدل؛ تونل‌سازی مکانیزه؛ FLAC3D؛ تاثیر مقدماتی Morris؛ فرامکعب لاتین | ||
| موضوعات | ||
| کاربرد شبکه عصبی در ژئو تکنیک؛ مهندسی تونل در محیط های خاکی و سنگی | ||
| عنوان مقاله [English] | ||
| Global Sensitivity Analysis in the Surface Settlement Prediction Caused by Mechanized Tunneling | ||
| نویسندگان [English] | ||
| leila nikakhtar1؛ S. Zare2؛ Hossein Mirzaei Nasirabad3 | ||
| 1Faculty of Mining, Petroleum & Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran | ||
| 2Department of Petrol um, Mining and Geo physic, Shahrood University of Technology | ||
| 3Department of Mining Engineering, Sahand University of Technology, Tabriz, Iran | ||
| چکیده [English] | ||
| Global sensitivity analysis is one of the beneficial and useful tools to identify the uncertainty of input variables that has been extremely investigated in different science such as simulations. Sensitivity analysis is an essential step in the production of a meta-model, which by identifying effective parameters in tunneling, reduces the time and computations required. In this paper, sensitivity analysis was carried out on geotechnical and operational parameters of EPB mechanized tunneling in soft soil. So, the tunneling processes were modeled using a finite difference method in FLAC 3D software, and the numerical model was validated by the monitoring data obtained from the East-West route of the Tehran metro 7 line. The sensitivity analysis by using the elementary effect Morris method was performed on the 6 input parameters and three parameters (face pressure, specific gravity and cohesion of the soil layer in which the tunnel was excavated) were selected as effective and sensitive parameters in the maximum surface settlement. Then to construct the meta-model, 100 samples were generated from effective parameters using the Latin Hypercube method. After numerical simulation for each sample, the simulation results were used for surface settlement prediction by using an artificial neural network. The results showed that the prediction of the meta-model based on the artificial neural network and the numerical model for the data in the design phase corresponded about 98%. | ||
| کلیدواژه‌ها [English] | ||
| Meta-model, Mechanized tunneling, FLAC 3D, Latin hypercube, Morris method | ||
| مراجع | ||
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[1] J. Ninić, J. Stascheit, G. Meschke, Simulation-based steering for mechanized tunneling using an ANN-PSO-based meta-model, in: Proceedings of the third international conference on soft computing technology in civil, structural and environmental engineering. Stirlingshire (Scotland), 2013, pp. 1-19. [2] J. Ninić, S. Freitag, G. Meschke, A hybrid finite element and surrogate modelling approach for simulation and monitoring supported TBM steering, Tunnelling and Underground Space Technology, 63 (2017) 12-28. [3] J. Meier, T. Schanz, Benchmarking of optimization algorithms,(2015). [4] X.-T. Feng, B.-R. Chen, C. Yang, H. Zhou, X. Ding, Identification of visco-elastic models for rocks using genetic programming coupled with the modified particle swarm optimization algorithm, International Journal of Rock Mechanics and Mining Sciences, 43(5) (2006) 789-801. [5] M. Calvello, R.J. Finno, Selecting parameters to optimize in model calibration by inverse analysis, Computers and Geotechnics, 31(5) (2004) 410-424. [6] S. Miro, D. Hartmann, T. Schanz, Global sensitivity analysis for subsoil parameter estimation in mechanized tunneling, Computers and Geotechnics, 56 (2014) 80-88. [7] T. Kasper, G. Meschke, A 3D finite element simulation model for TBM tunnelling in soft ground, International journal for numerical and analytical methods in geomechanics, 28(14) (2004) 1441-1460. [8] F. Nagel, G. Meschke, An elasto‐plastic three phase model for partially saturated soil for the finite element simulation of compressed air support in tunnelling, International journal for numerical and analytical methods in geomechanics, 34(6) (2010) 605-625. [9] J.P. Kleijnen, Design and analysis of simulation experiments, in: International Workshop on Simulation, Springer, 2015, pp. 3-22. [10] N. Do, D. Dias, P. Oreste, Numerical analysis of segmental tunnel lining under seismic loads, 2015. [11] X.-m. Song, F.-z. Kong, C.-s. Zhan, J.-w. Han, X.-h. Zhang, Parameter identification and global sensitivity analysis of Xin'anjiang model using meta-modeling approach, Water Science and Engineering, 6(1) (2013) 1-17. [12] C. Zhao, A.A. Lavasan, T. Barciaga, V. Zarev, M. Datcheva, T. Schanz, Model validation and calibration via back analysis for mechanized tunnel simulations–The Western Scheldt tunnel case, Computers and Geotechnics, 69 (2015) 601-614. [13] N.A. Nariman, R.R. Hussain, I.I. Mohammad, P. Karampour, Global sensitivity analysis of certain and uncertain factors for a circular tunnel under seismic action, Frontiers of Structural and Civil Engineering, 13(6) (2019) 1289-1300. [14] Y. Fang, Y. Su, On the use of the global sensitivity analysis in the reliability-based design: Insights from a tunnel support case, Computers and Geotechnics, 117 (2020) 103280. [15] C. Blom, E. Van der Horst, P. Jovanovic, Three-dimensional structural analyses of the shield-driven “Green Heart” tunnel of the high-speed line south, Tunnelling and Underground Space Technology, 14(2) (1999) 217-224. [16] A. Lambrughi, L.M. Rodríguez, R. Castellanza, Development and validation of a 3D numerical model for TBM–EPB mechanised excavations, Computers and Geotechnics, 40 (2012) 97-113. [17] H. Chakeri, Y. Ozcelik, B. Unver, Effects of important factors on surface settlement prediction for metro tunnel excavated by EPB, Tunnelling and Underground Space Technology, 36 (2013) 14-23. [18] H. Lai, H. Zheng, R. Chen, Z. Kang, Y. Liu, Settlement behaviors of existing tunnel caused by obliquely under-crossing shield tunneling in close proximity with small intersection angle, Tunnelling and Underground Space Technology, 97 (2020) 103258. [19] S. Freitag, B.T. Cao, J. Ninić, G. Meschke, Hybrid surrogate modelling for mechanised tunnelling simulations with uncertain data, International Journal of Reliability and Safety, 9(2-3),(2015)154 -173. [20] K. Khaledi, T. Schanz, S. Miro, Application of metamodelling techniques for mechanized tunnel simulation, Journal of Theoretical and Applied Mechanics, 44(1) (2014) 45-54. [21] M.C. Hill, C.R. Tiedeman, Effective groundwater model calibration: with analysis of data, sensitivities, predictions, and uncertainty, John Wiley & Sons, 2006. [22] F. Pianosi, K. Beven, J. Freer, J.W. Hall, J. Rougier, D.B. Stephenson, T. Wagener, Sensitivity analysis of environmental models: A systematic review with practical workflow, Environmental Modelling & Software, 79 (2016) 214-232. [23] B.Hamraz, A.Akbarpour, b.Porreza, Uncertainty analysis of MODFLOW input parameters by GLUE method ( case study: Birgand plain), Journal of Soil and Water Protection Research, 22(6) (2016) 61-79. [24] A. Saltelli, Sensitivity analysis for importance assessment, Risk analysis, 22(3) (2002) 579-590. [25] J.C. Helton, F.J. Davis, Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems, Reliability Engineering & System Safety, 81(1) (2003) 23-69. [26] M.D. McKay, R.J. Beckman, W.J. Conover, Comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics, 21(2) (1979) 239-24. [27] J. Ninić, Computational strategies for predictions of the soil-structure interaction during mechanized tunneling, PHD thesis 2016. [28] J. Ninic, J. Stascheit, G. Meschke, Prediction of tunnelling induced settlements using simulation-based artificial neural networks, in: Proceedings of the 2nd International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering, paper, 2011. [29] R. Heidari sheibani, S. Zare, H. Mirzaui nasirabad, M. Foroughi, Numerical Study of Face Pressure Effect on Surface Settlement in Soft Ground Mechanized Tunneling-A Case Study: Tehran Metro Line 7(in persian), Tunneling & Underground Space Engineering (TUSE), 1(1) (2013) 67-57. [30] X. Cao, S. Yan, Numerical analysis for earthquake dynamic responses of tunnel with different lining rigidity based on finite element method, Information Technology Journal, 12(13) (2013) 2599-2604. [31] SCE, Engineering geology report of East-West section ( Tehran Metro Line 7), SCE.(In Persian), (2011). [32] SCE, Monitoring report of East-West section ( Tehran Metro Line 7), SCE.(In Persian), (2012). [33] J. Ninić, G. Meschke, Model update and real-time steering of tunnel boring machines using simulation-based meta models, Tunnelling and Underground Space Technology, 45 (2015) 138-152. [34] F. Pianosi, F. Sarrazin, T. Wagener, A Matlab toolbox for global sensitivity analysis, Environmental Modelling & Software, 70 (2015) 80-85. | ||
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