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جعفرزاده, احمد, صابری‌نصر, امیر, هاشم‌نژاد, آرش, مرسلی, مسعود. (1401). تحلیل عدم‌قطعیت شبیه‌سازی عددی هجوم آب به تونل انتقال آب صفارود کرمان. , 12(4), 675-694. doi: 10.22059/jwim.2022.339210.969
احمد جعفرزاده; امیر صابری‌نصر; آرش هاشم‌نژاد; مسعود مرسلی. "تحلیل عدم‌قطعیت شبیه‌سازی عددی هجوم آب به تونل انتقال آب صفارود کرمان". , 12, 4, 1401, 675-694. doi: 10.22059/jwim.2022.339210.969
جعفرزاده, احمد, صابری‌نصر, امیر, هاشم‌نژاد, آرش, مرسلی, مسعود. (1401). 'تحلیل عدم‌قطعیت شبیه‌سازی عددی هجوم آب به تونل انتقال آب صفارود کرمان', , 12(4), pp. 675-694. doi: 10.22059/jwim.2022.339210.969
جعفرزاده, احمد, صابری‌نصر, امیر, هاشم‌نژاد, آرش, مرسلی, مسعود. تحلیل عدم‌قطعیت شبیه‌سازی عددی هجوم آب به تونل انتقال آب صفارود کرمان. , 1401; 12(4): 675-694. doi: 10.22059/jwim.2022.339210.969

تحلیل عدم‌قطعیت شبیه‌سازی عددی هجوم آب به تونل انتقال آب صفارود کرمان

مدیریت آب و آبیاری
مقاله 1، دوره 12، شماره 4، بهمن 1401، صفحه 675-694    اصل مقاله (3.05 M)
نوع مقاله: مقاله پژوهشی
شناسه دیجیتال (DOI): 10.22059/jwim.2022.339210.969
نویسندگان
احمد جعفرزاده* 1؛ امیر صابری‌نصر2؛ آرش هاشم‌نژاد3؛ مسعود مرسلی4
1گروه علوم و مهندسی آب، دانشگاه بیرجند، بیرجند، ایران.
2گروه زمین‌شناسی، دانشگاه شهید چمران اهواز، اهواز، ایران.
3زمین‌شناسی مهندسی، شرکت ساحل امید ایرانیان، تهران، ایران.
4گروه زمین‌شناسی، دانشگاه اصفهان، اصفهان، ایران.
چکیده
یکی از مهم‌ترین مشکلات در پروسه حفاری تونل، هجوم آب به درون تونل است. از این‌رو، تخمین میزان هجوم آب و ‏پیش‌بینی اقدامات موردنیاز قبل از شروع حفاری بسیار اهمیت دارد. این پژوهش در نظر دارد تا یک مدل بر پایه روش عددی بدون ‏شبکه و الگوریتم ‏DiffeRential Evolution Adaptive Metropolis (DREAM)‎‏ را جهت انجام‌دادن فرایند شبیه‌سازی- ‏بهینه‌سازی هجوم آب به درون تونل ارائه دهد. در طرح پیشنهادی، ارتفاع سطح آب زیرزمینی در محیط پیرامون تونل انتقال آب ‏صفارود کرمان شبیه‌سازی شد و تحلیل عدم‌قطعیت پارامترها (هدایت هیدرولیکی)، داده‌های ورودی (بارش و دبی منابع برداشت) ‏و مدل‌سازی عددی در نظر گرفته شد. نتایج تحلیل عدم‌قطعیت نشان داد که هدایت هیدرولیکی در نواحی مختلف بین 0002/0 تا ‏‏2/0 متر بر روز متغیر است. هم‌چنین، بررسی وضعیت زمین‌شناسی منطقه اثبات کرد که شرایط هیدروترمال موجب افزایش ‏نفوذپذیری شده است. علاوه بر این، مشخص شد که داده‌های ورودی با چهار درصد کم تخمینی ثبت شده‌اند. بررسی عدم‌قطعیت ‏مؤلفه‌های دخیل در مدل‌سازی عددی نیز نشان داد که برای حصول یک دقت مناسب، اندازه دامنه محلی و دامنه حمایتی باید ‏به‌ترتیب 85/0 و سه برابر فاصله میان‌گرهی باشند. نتایج به‌دست‌آمده از شبیه‌سازی ارتفاع سطح آب زیرزمینی با استفاده از ‏خروجی فرایند شبیه‌سازی- بهینه‌سازی نشان داد که بین مقادیر مشاهداتی و شبیه‌سازی شده دقت مناسبی وجود دارد و از این ‏منظر شاخص ‏RMSE‏ حدود 5731/2 متر برآورد شد. علاوه‌بر این، مقایسه شبیه‌سازی هجوم آب نشان داد که دبی هجوم آب به ‏درون تونل در قسمت‌های شمالی و جنوبی به‌ترتیب معادل 43/72 و 09/45 لیتر بر ثانیه است.‏
کلیدواژه‌ها
آبخوان سنگی درز و شکاف‌دار؛ بدون شبکه؛ تابع محلی درون‌یاب نقطه‌ای شعاعی؛ دگرسانی؛ شرایط هیدروترمال
عنوان مقاله [English]
Uncertainty analysis of numerical simulation of groundwater inflow into Safarood Kerman water transfer tunnel
نویسندگان [English]
Ahmad Jafarzadeh1؛ Amir Saberinasr2؛ Arash Hashemnejad3؛ Massoud Morsali4
1Department of Water Resources, University of Birjand, Birjand, Iran.
2Department of Geology, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
3Geology Engineering, Sahel Omid Iranian Consulting Engineers Company, Tehran, Iran.
4Department of Geology, Isfahan University of Technology, Isfahan, Iran.
چکیده [English]
Groundwater inflow is one of the most important problems in Constructing a conveyance tunnel. Increasing pressure on the tunnel wall and reducing its stability, the related issues of drainage and pumping, destructive impacts on the mechanical and geological condition of the tunnel surrounding environment, loss of life, increased costs, and advance delays are among the most important challenges that can be existed during excavation. Therefore, it is crucial to evaluate the amount of water inflow and predict the required measures previously. Conventional techniques for estimating the water inflow are analytical-experimental techniques whose efficiency in complex heterogeneous and anisotropic aquifers is always tainted. Accordingly, this study intends to investigate the effectiveness of the Meshfree (Mfree) numerical method for simulating the groundwater level in the environment surrounding the Safarood water transfer tunnel in Kerman. Also, considering the uncertainty analysis, uncertainty of parameters (hydraulic conductivity), input data, and structure of numerical modeling were addressed using DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm. Hence, an open-source framework based on a Mfree numerical method and DREAM algorithm was proposed for the simulation-optimization process of groundwater level prediction in the environment surrounding the tunnel, and finally, the water inflow discharge was estimated. The results of uncertainty analysis indicated that hydraulic conductivity parameters may be ranged between 0.0002 to 0.2 m/day in different homogeneous zones. Also, the study of thin sections samples collected from field observation shows that hydrothermal conditions have influenced directly the alteration of rocks and minerals in some zones and likely it is the main factor in increasing permeability in these areas. The results showed that the recorded input data has a four percent underestimation. The uncertainty of the parameters involved with the structure of numerical modeling also proved that to obtain an adequate accuracy, the size of the local domain must be about 0.85, and the support domain should be considered at least three nodes to estimate the weight function. The simulation results of groundwater level fluctuations using the derived true values of parameters showed that there is a good accuracy between the observed and simulated values (the RMSE index was estimated to be about 2.531 meters). In addition, the assessment of numerical simulation of groundwater inflow into tunnel indicated that inflow rate in the north and south parts is respectively 72.43 and 09.45 l/s.
کلیدواژه‌ها [English]
Alteration, Fractured-Rock Aquifer, Hydrothermal Conditions, Local Radial Point Interpolation Function, Mesh less
مراجع
  1. Anshuman, A., & Eldho, T. I. (2019). Modeling of transport of first-order reaction networks in porous media using meshfree radial point collocation method. Computational Geosciences, 23(6), 1369-1385. https://doi.org/10.1007/s10596-019-09906-8
  2. Arnold, J. G., Allen, P. M., & Bernhardt, G. (1993). A comprehensive surface-groundwater flow model. Journal of hydrology, 142(1-4), 47-69.
  3. Bouvard, M., & Pinto, N. (1969). Aménagement Caprivari-Cahoeira étude en charge. La Houille Blanche, (7), 747-760.
  4. Chiu, Y. C., & Chia, Y. (2012). The impact of groundwater discharge to the Hsueh-Shan tunnel on the water resources in northern Taiwan. Hydrogeology journal, 20(8), 1599-1611.
  5. Coli, N., Pranzini, G., Alfi, A., & Boerio, V. (2008). Evaluation of rock-mass permeability tensor and prediction of tunnel inflows by means of geostructural surveys and finite element seepage analysis. Engineering Geology, 101(3-4), 174-184.
  6. Crist, J. (2019). Advantages of Mesh Free Methods for Structural and Fluid Analysis (No. 2019-01-0939). SAE Technical Paper.
  7. Dassargues A (1997) Groundwater modelling to predict the impact of tunnel on the behavior of water table aquiefer in urban condition. In: Groundwater in the urban environment: processes and manage- ment. Balkema, Rotterdam, The Netherlands, pp. 225-230.
  8. El Tani, M. (1999). Water inflow into tunnels. Proceedings of the World Tunnel Congress ITA-AITES. p. 61-70.
  9. El Tani, M. (2003). Circular tunnel in a semi-infinite aquifer. Tunnelling and Underground Space Technology, 18(1), 49-55.
  10. Farhadian, H., & Nikvar-Hassani, A. (2019). Water flow into tunnels in discontinuous rock: a short critical review of the analytical solution of the art. Bulletin of Engineering Geology and the Environment, 78(5), 3833-3849.
  11. Farhadian, H., Katibeh, H., & Huggenberger, P. (2016). Empirical model for estimating groundwater flow into tunnel in discontinuous rock masses. Environmental Earth Sciences, 75(6), 471.
  12. Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical science, 7(4), 457-472.
  13. Gholizadeh, H., Peely, A. B., Karney, B. W., & Malekpour, A. (2020). Assessment of groundwater ingress to a partially pressurized water-conveyance tunnel using a conduit-flow process model: a case study in Iran. Hydrogeology Journal, 28(7), 2573-2585.
  14. Ghorbani, E., Ghadernejad, S., Emami, D., & Nejati, H. (2021). Estimating groundwater inflow into Dorud-Khorramabad railway tunnel using analytical and numerical methods. International Journal of Mining and Geo-Engineering, 55(1), 33-41. doi: 10.22059/ijmge.2020.306044.594856
  15. Gingold, R. A., & Monaghan, J. J. (1977). Smoothed particle hydrodynamics: theory and application to non-spherical stars. Monthly Notices of the Royal Astronomical Society, 181(3), 375-389.
  16. Golian, M., Teshnizi, E. S., & Nakhaei, M. (2018). Prediction of water inflow to mechanized tunnels during tunnel-boring-machine advance using numerical simulation. Hydrogeology Journal, 26(8), 2827-2851.
  17. Golian, M., Teshnizi, E. S., Parise, M., Terzić, J., Milanović, S., Vakanjac, V. R., ... & Saadat, H. (2021). A new analytical method for determination of discharge duration in tunnels subjected to groundwater inrush. Bulletin of Engineering Geology and the Environment, 80(4), 3293-3313.
  18. Gong, Q. M., Yin, L. J., & She, Q. R. (2013). TBM tunneling in marble rock masses with high in situ stress and large groundwater inflow: a case study in China. Bulletin of Engineering Geology and the Environment, 72(2), 163-172.
  19. Goodman, R., Moye, D., Schalkwyk, A., & Javendel, I. (1965). Groundwater inflow during tunnel driving. Engineering Geology, 1, 150-162
  20. Hamraz, B., Akbarpour, A., Bilondi, M. P., & Tabas, S. S. (2015). On the assessment of ground water parameter uncertainty over an arid aquifer. Arabian journal of Geosciences, 8(12), 10759-10773.
  21. Han, Z., Lu, W., & Lin, J. (2020). Uncertainty analysis for precipitation and sea-level rise of a variable-density groundwater simulation model based on surrogate models. Environmental Science and Pollution Research, 27(22), 28077-28090.
  22. Hassani, A. N., Farhadian, H., & Katibeh, H. (2018). A comparative study on evaluation of steady-state groundwater inflow into a circular shallow tunnel. Tunnelling and Underground Space Technology, 73, 15-25.
  23. Hassani, A. N., Katibeh, H., & Farhadian, H. (2016). Numerical analysis of steady-state groundwater inflow into Tabriz line 2 metro tunnel, northwestern Iran, with special consideration of model dimensions. Bulletin of Engineering Geology and the Environment, 75(4), 1617-1627.
  24. Hassanzadeh, Y., Afshar, A.A., Pourreza-Bilondi, M., Memarian, H., & Besalatpour, A.A. (2019). Toward a combined Bayesian frameworks to quantify parameter uncertainty in a large mountainous catchment with high spatial variability. Environ Monit Assess, 191(1), 23.
  25. Heuer, R. E. (1995, June). Estimating rock tunnel water inflow. In Proceedings of the rapid excavation and tunneling conference (pp. 41-60). SOCIETY FOR MINING, METALLOGY & EXPLORATION, INC.
  26. Huangfu, M., Wang, M. S., Tan, Z. S., & Wang, X. Y. (2010). Analytical solutions for steady seepage into an underwater circular tunnel. Tunnelling and Underground Space Technology, 25(4), 391-396.
  27. Hydrogeochemical studies of Safarood Kerman water transfer tunnel. (2018). Sahel Omid Iranian consulting engineers company. (in Persian).
  28. Hydrogeological studies of Safarood Kerman water transfer tunnel. (2022). Sahel Omid Iranian consulting engineers company. (in Persian).
  29. Insana, A., Barla, M., & Sulem, J. (2020). Energy tunnel linings thermo-mechanical performance: comparison between field observations and numerical modelling. In E3S Web of Conferences (Vol. 205, p. 06008). EDP Sciences.
  30. Jafarzadeh, A., Khashei-Siuki, A., & Pourreza-Bilondi, M. (2021a). Performance assessment of model averaging techniques to reduce structural uncertainty of groundwater modeling. Water Resources Management, 1-25.
  31. Jafarzadeh, A., Pourreza-Bilondi, M., Akbarpour, A., Khashei-Siuki, A., & Samadi, S. (2021b). Application of multi-model ensemble averaging techniques for groundwater simulation: synthetic and real-world case studies. Journal of Hydroinformatics, 23(6), 1271-1289.
  32. Jafarzadeh, A., Khashei, A., & PurrezaBilondi, M. (2021). Performance Assessment of Numerical Solutions in Groundwater Simulation (case study: Birjand aquifer). (in Persian).
  33. Katibeh, H., & Aalianvari, A. (2012). Common Approximations to the water inflow into Tunnels. Drainage systems, 75-88.
  34. Katibeh, H., & Aalianvari, A. (2009). Development of a New Method for Tunnel Site Rating from. Journal of Applied Sciences, 9(8), 1496-1502.
  35. Kim, S. M., Yang, H. Y., & Yoon, S. G. (2008). Environmental problems of groundwater around the longest expressway tunnel in Korea. In Geotechnical Aspects of Underground Construction in Soft Ground. (pp. 415-420). CRC Press.
  36. Kolymbas, D., & Wagner, P. (2007). Groundwater ingress to tunnels–the exact analytical solution. Tunnelling and Underground Space Technology, 22(1), 23-27.
  37. Lee, I. M., & Nam, S. W. (2001). The study of seepage forces acting on the tunnel lining and tunnel face in shallow tunnels. Tunnelling and Underground Space Technology, 16(1), 31-40.
  38. Lei, S. (1999). An analytical solution for steady flow into a tunnel. Ground Water, 37(1), 23.
  39. Liu, F., Xu, G., Huang, W., & Hu Sh, H. M. (2012). The effect of grouting reinforcement on groundwater seepage in deep tunnels. Blucher Mech Eng Proc, 1(1), 4727-4737.
  40. Liu, G. R. (2009). Meshfree methods: moving beyond the finite element method. CRC press.
  41. Liu, G. R., & Gu, Y. T. (2005). An introduction to meshfree methods and their programming. Springer Science & Business Media.
  42. Lombardi, G. (2002). quoted from: El Tani M (2003) Circular tunnel in a semi-infinite aquifer. Tunneling and Underground Space Technology, 18, 49-55.
  43. Maréchal, J. C., Lanini, S., Aunay, B., & Perrochet, P. (2014). Analytical solution for modeling discharge into a tunnel drilled in a heterogeneous unconfined aquifer. Groundwater, 52(4), 597-605.
  44. Marshall, A. M., Link, T. E., Flerchinger, G. N., & Lucash, M. S. (2021). Importance of parameter and climate data uncertainty for future changes in boreal hydrology. Water Resources Research, 57(8), e2021WR029911.
  45. Meiri, D. (1985). Unconfined groundwater flow calculation into a tunnel. Journal of Hydrology, 82(1-2), 69-75.
  46. Mohtashami, A., Akbarpour, A., & Mollazadeh, M. (2017). Development of two-dimensional groundwater flow simulation model using meshless method based on MLS approximation function in unconfined aquifer in transient state. Journal of Hydroinformatics, 19(5), 640-652.
  47. Molinero, J., Samper, J., & Juanes, R. (2002). Numerical modeling of the transient hydrogeological response produced by tunnel construction in fractured bedrocks. Engineering Geology, 64(4), 369-386.
  48. Mustafa, S. M. T., Nossent, J., Ghysels, G., & Huysmans, M. (2020). Integrated Bayesian Multi-model approach to quantify input, parameter and conceptual model structure uncertainty in groundwater modeling. Environmental Modelling & Software, 126, 104654.
  49. Mustafa, S. T., Nossent, J., Ghysels, G., & Huysmans, M. (2020). Integrated Bayesian Multi-model approach to quantify input, parameter and conceptual model structure uncertainty in groundwater modeling. Environmental Modelling and Software, 126. 104654. https://doi.org/10.1016/j.envsoft.2020.104654
  50. Pan, Y., Zeng, X., Xu, H., Sun, Y., Wang, D., & Wu, J. (2020). Assessing human health risk of groundwater DNAPL contamination by quantifying the model structure uncertainty. J Hydrol, 584, 124690.
  51. Park, K. H., Owatsiriwong, A., & Lee, J. G. (2008). Analytical solution for steady-state groundwater inflow into a drained circular tunnel in a semi-infinite aquifer: a revisit. Tunnelling and Underground Space Technology, 23(2), 206-209.
  52. Pham, H. V., & Tsai, F. T. C. (2016). Optimal observation network design for conceptual model discrimination and uncertainty reduction. Water Resources Research, 52(2), 1245-1264.
  53. Rajabi, M. M., Ataie-Ashtiani, B., & Simmons, C. T. (2018). Model-data interaction in groundwater studies: Review of methods, applications and future directions. Journal of hydrology, 567, 457-477.
  54. Rat, M. (1973). Ecoulement et Repartition des Pressions Interstitielles Autour des Tunnels. Bull Liaison Lab Ponts Chauss, (68).
  55. Ribacchi, R., Graziani, A., & Boldini, D. (2002). Previsione degli afflussi d’acqua in galleria ed influenza sull’ambiente. Le Opere in Sotterraneo e il Rapporto con l’Ambiente, 143-199.
  56. Saberinasr, A., Morsali, M., Hashemnejad, A., & Hassanpour, J. (2019). Determining the origin of groundwater elements using hydrochemical data (case study: Kerman water conveyance tunnel). Environmental Earth Sciences, 78(6), 1-17.
  57. Scheidler, S., Huggenberger, P., Butscher, C., & Dresmann, H. (2019). Tools to simulate changes in hydraulic flow systems in complex geologic settings affected by tunnel excavation. Bulletin of Engineering Geology and the Environment, 78(2), 969-980.
  58. Shoemaker, W. B., Kuniansky, E. L., Birk, S., Bauer, S., & Swain, E. D. (2008). Documentation of a conduit flow process (CFP) for MODFLOW-2005.
  59. Song, W. K., Hamm, S. Y., & Cheong, J. Y. (2006). Estimation of groundwater discharged into a tunnel. Tunnelling and Underground Space Technology incorporating Trenchless Technology Research, 3(21), 460.
  60. Swetha, K., Eldho, T. I., Singh, L. G., & Kumar, A. V. (2022). Groundwater flow simulation in a confined aquifer using Local Radial Point Interpolation Meshless method (LRPIM). Engineering Analysis with Boundary Elements, 134, 637-649.
  61. Tedesco, G., Bonduà, S., Borgatti, L., Bossi, G., Fabbri, P., Piccinini, L., & Marcato, G. (2019). Slope and Groundwater Monitoring for 3D Numerical Modelling to Ensure the Structural Health of an Alpine Road Tunnel Crossing an Active Rock Slide. Multidisciplinary Digital Publishing Institute Proceedings, 30(1), 12.
  62. Thomas, A., Majumdar, P., Eldho, T. I., & Rastogi, A. K. (2018). Simulation optimization model for aquifer parameter estimation using coupled meshfree point collocation method and cat swarm optimization. Engineering Analysis with Boundary Elements, 91, 60-72.
  63. Tseng, D. J., Tsai, B. R., & Chang, L. C. (2001). A case study on ground treatment for a rock tunnel with high groundwater ingression in Taiwan. Tunnelling and Underground Space Technology, 16(3), 175-183.
  64. Vrugt, J. A., Diks, C. G., Gupta, H. V., Bouten, W., & Verstraten, J. M. (2005). Improved treatment of uncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation. Water resources research, 41(1).
  65. Vrugt, J. A., Ter Braak, C. J. F., Diks, C. G. H., Robinson, B. A., Hyman, J. M., & Higdon, D. (2009). Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling. International Journal of Nonlinear Sciences and Numerical Simulation, 10(3), 273-290.
  66. Wu, J., & Zeng, X. (2013). Review of the uncertainty analysis of groundwater numerical simulation. Chin Sci Bull, 58(25), 3044-3052.
  67. Xia, Q., Xu, M., Zhang, H., Zhang, Q., & Xiao, X. X. (2018). A dynamic modeling approach to simulate groundwater discharges into a tunnel from typical heterogenous geological media during continuing excavation. KSCE Journal of Civil Engineering, 22(1), 341-350.
  68. Xue, L., & Zhang, D. (2014). A multimodel data assimilation framework via the ensemble Kalman filter. Water Resources Research, 50(5), 4197-4219.
  69. Yang, F. R., Lee, C. H., Kung, W. J., & Yeh, H. F. (2009). The impact of tunneling construction on the hydrogeological environment of “Tseng-Wen Reservoir Transbasin Diversion Project” in Taiwan. Engineering Geology, 103(1-2), 39-58.
  70. Yoon, H., Hart, D. B., & McKenna, S. A. (2013). Parameter estimation and predictive uncertainty in stochastic inverse modeling of groundwater flow: Comparing null‐space Monte Carlo and multiple starting point methods. Water Resources Research, 49(1), 536-553.
  71. Zhang, E., Jaramillo, C. A., & Feldsher, T. B. (2007). Transient simulation of groundwater flow for tunnel construction using time-variable boundary condition. In Fall Meeting Google Scholar, American Geophysical Union, Washington, DC.
  72. Zhou, J. Q., Liu, H. B., Li, C., He, X. L., Tang, H., & Zhao, X. J. (2021). A semi-empirical model for water inflow into a tunnel in fractured-rock aquifers considering non-Darcian flow. Journal of Hydrology, 597, 126149.
آمار
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