Geomechanics and Engineering

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The investigation of the applicability of Monte Carlo Simulation in analyzing TBM project requirements

  • Ulku Kalayci Sahinoglu (Istanbul University-Cerrahpasa, Faculty of Engineering, Department of Mining Engineering)
  • Received : 2024.06.14
  • Accepted : 2024.08.14
  • Published : 2024.10.10
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Abstract

Geotechnical parameter estimation is critical to the design, performance, safety, and cost and schedule management in Tunnel Boring Machine projects. Since these parameters vary within a certain range, relying on mean values for evaluation introduces significant risks to the project. Due to the non-homogeneous characteristics of geological formation, data may not exhibit a normal distribution and the presence of outliers might be deceptive. Therefore, the use of reliable analyses and simulation models is inevitable in the course of the data evaluation process. Advanced modeling techniques enable comprehensive analysis of the project data and allowing to model the uncertainty in geotechnical parameters. This study involves using Monte Carlo Simulation method to predict probabilistic distributions of field data, and therefore, establish a basis for designs and in turn to minimize project risks. In the study, 166 sets of geotechnical data Obtained from 35 boreholes including Standard Penetration Test, Limit Pressure, Liquid Limit, and Plastic Limit values, which are mostly utilized parameters in estimating project requirements, were used to estimate the geotechnical data distribution of the study field. In this context, firstly, the data was subjected to multi-parameter linear regression and variance analysis. Then, the obtained equations were implemented into a Monte Carlo Simulation, and probabilistic distributions of the geotechnical data of the field were simulated and corresponding to the 90% probability range, along with the minimum and maximum values at the 5% probability levels presented. Accordingly, while the average SPT N30 value is 42.86, but the highest occurrence rate is 50.81. For Net Limit Pressure, the average field data is 17.07 kg/cm2, with the maximum occurrence between 9.6 kg/cm2 and 13.7 kg/cm2. Similarly, the average Plastic Limit value is 22.32, while the most probable value is 20.6. The average Liquid Limit value is 56.73, with the highest probability at 54.48, as indicated in the statistical data distribution. Understanding the percentage distribution of data likely to be encountered in the project allows for accurate forecasting of both high and low probability scenarios, offering a significant advantage, particularly in ordering TBM requirements.

Keywords

References

  1. Albatal, A., Mohammad, H. and Elrazik, M.A. (2013), "Effect of inadequate site investigation on the cost and time of a construction project", Geotech. Saf. Risk, IV, 331.
  2. Alberto-Hernandez, Y., Kang, C., Yi, Y. and Bayat, A. (2017), Clogging potential of tunnel boring machine (TBM): A review", Int. J. Geotech. Eng., 12(3), 316-323. https://doi.org/10.1080/19386362.2016.1277621.
  3. Amar, J.G. (2006), "The Monte Carlo method in science and engineering", Comput. Sci. Eng., 8(2), 9-19. https://doi.org/10.1109/MCSE.2006.34.
  4. ASTM D1586/D1586M-18 Standard Test Method for Standard Penetration Test (SPT) And Split-Barrel Sampling of Soils.
  5. ASTM D4318-17e1 Standard Test Methods for Liquid Limit, Plastic Limit, and Plasticity Index of Soils.
  6. Chen, W.K. (2013), "Theory and applications of Monte Carlo simulations", In Tech. http://doi.org/10.5772/45892.
  7. Chernick, M.R. and Friis, R.H. (2003), Introductory Biostatistics for the Health Sciences: Modern Applications Including Bootstrap (1st Ed.), Wiley-Interscience.
  8. Di Giulio, A., Sebastiani, D. and. Miliziano, S. (2018), "Effect of chemicals in clogging risk reduction for TBM-EPB application", Proceedings of the World Tunnel Congress 2018-The Role of Underground Space in Building Future Sustainable Cities.
  9. Ferson, S. (1996), "What Monte Carlo methods cannot do", Human Ecol. Risk Assessment, 2(4), 990-1007. https://doi.org/10.1080/10807039609383659.
  10. Gultekin, N.Y. and Dogan, A. (2022), "Predicting net limit pressure and deformation modulus of cohesive soils using machine learning-based methods", NOHU J. Eng. Sci., 11(4), 1025-1033. https://doi.org/10.28948/ngumuh.1155568.
  11. Haeri, S.M. (2016), "The role of geotechnical engineering in sustainable and resilient cities", Trans. Civil Eng., 1658-1674. https://doi.org/10.24200/SCI.2016.2237.
  12. Harichane, Z., Guellil, M.E. and Gadouri, H. (2018), "Benefits of Probabilistic Soil-Foundation-Structure Interaction Analysis", Int. J. Geotech. Earthq. Eng., 9(1), 42-64. http://doi.org/10.4018/IJGEE.2018010103.
  13. Hollmann, F.S. and Thewes, M. (2013), "Assessment method for clay clogging and disintegration of fines in mechanised tunnelling", Tunn. Undergr. Sp. Tech., 37, 96-106. https://doi.org/10.1016/j.tust.2013.03.010.
  14. Imm (2011), Istanbul Metropolitan Municipality, Earthquake Risk Management and Urban Improvement Department, Earthquake and Soil Inspection Directorate, Urban Geology Project, Istanbul, Turkiye.
  15. Jiao, K., Han, D., Li, J., Bai, B., Gong, L. and Yu, B. (2021), "A novel LBM-DEM based pore-scale thermal-hydro-mechanical model for the fracture propagation process", Comput. Geotech., 139, 104418. https://doi.org/10.1016/j.compgeo.2021.104418.
  16. Kim, M., Chung, C.K., Han, J.W. and Kim, H.S. (2023), "Three-dimensional geostatistical modeling of subsurface stratification and SPT-N Value at dam site in South Korea", Geomech. Eng., 34(1), 29-41. https://doi.org/10.12989/gae.2023.34.1.029.
  17. Kwon, Y.M., Chang, I., Lee, M. and Cho, G.C. (2019), "Geotechnical engineering behavior of biopolymer-treated soft marine soil", Geomech. Eng., 17(5), 453-464. https://doi.org/10.12989/gae.2019.17.5.453.
  18. Li, T.Z. and Yang, X.L. (2019), "Probabilistic analysis for face stability of tunnels in Hoek-Brown media", Geomech. Eng., 18(6), 595-603. https://doi.org/10.12989/gae.2019.18.6.595.
  19. Likitlersuang, S., Surarak, C., Wanatowski, D., Oh, E. and Balasubramaniam, A. (2013), "Geotechnical parameters from pressuremeter tests for MRT Blue Line extension in Bangkok", Geomech. Eng., 5(2), 99-118. https://doi.org/10.12989/gae.2013.5.2.099.
  20. Look, B. (2022), "Managing geotechnical uncertainty with simulation models: An introduction", Australian Geomech. J., https://doi.org/10.56295/AGJ5741.
  21. Lu, Q., Liu, S., Mao, W., Yu, Y. and Long, X. (2024), "A numerical simulation-based ANN method to determine the shear strength parameters of rock minerals in nanoscale", Comput. Geotech., 169, 106175. https://doi.org/10.1016/j.compgeo.2024.106175.
  22. Mair, R., Merritt, A., Borghi, X., Yamazaki, H. and Minami, T. (2003), "Soil conditioning for clay soils", Tunn. Tunn. Int., 35(4), 29-33.
  23. Marek, P., Brozzetti, J., Gustar, M. and Elishakoff, I. (2002), "Probabilistic assessment of structures using Monte Carlo Simulations", ASME. Appl. Mech. Rev. March., 55(2), 31-32. https://doi.org/10.1115/1.1451167.
  24. Milligan, G. (2000), Lubrication and soil conditioning in tunnelling, pipe jacking and microtunnelling: A state-of-theart review. Geotechnical Consulting Group, London, UK.
  25. Lu, D., Ma, C., Du, X., Jin, L. and Gong, Q. (2016), "Development of a new nonlinear unified strength theory for geomaterials based on the characteristic stress concept", Int. J. Geomech., 17(2). https://doi.org/10.1061/(ASCE)GM.1943-5622.0000729.
  26. Peng, X., Li, D.Q. and Cao, Z.J. (2017), "Reliability-based robust geotechnical design using Monte Carlo simulation", Bull. Eng. Geol. Environ., 76, 1217-1227. https://doi.org/10.1007/s10064-016-0905-3.
  27. Sari Ahmed, B., Gadouri, H., Ghrici, M. and Harichane, K. (2018), "Best-fit models for predicting the geotechnical properties of FA-stabilised problematic soils used as materials for earth structures", Int. J. Pavement Eng., 21(7), 939-953. https://doi.org/10.1080/10298436.2018.1517874.
  28. Tang, V.T., Vu, T.K., Huded, P.M. and Nguyen, T.L.C. (2024), "Correlation between SPT indexes of soils to pressing pile load and settlement of concrete piles: An experimental study in Bac Giang, Vietnam", (Eds., Duc Long, P. and Dung, N.T.) Proceedings of the 5th International Conference on Geotechnics for Sustainable Infrastructure Development. GEOTEC 2023. Lecture Notes in Civil Engineering, 395, Springer, Singapore. https://doi.org/10.1007/978-981-99-9722-0_17.
  29. Tu, H., Zhou, H., Gao, Y., Lu, J., Singh, H. K., Zhang, C., Hu, D., and Hu, M. (2021), "Probability analysis of deep tunnels based on Monte Carlo Simulation: Case study of diversion tunnels at Jinping II hydropower station, Southwest China", Int. J. Geomech., 21(12). https://doi.org/10.1061/(ASCE)GM.1943-5622.0002146
  30. Vargas, J.P., Koppe, J.C. and Perez, S. (2014), "Monte Carlo Simulation as a tool for tunneling planning", Tunn. Undergr. Sp. Tech., 40, 203-209. https://doi.org/10.1016/j.tust.2013.10.011.
  31. Xiang, P., Tianji, W., Jiquan, L., Shaofei, J., Chani, Q. and Bing, Y. (2017), "Hybrid reliability analysis with uncertain statistical variables, sparse variables and interval variables", Eng. Optim., 50, 1-17. https://doi.org/10.1080/0305215X.2017.1400025.
  32. Yavas, F. (2008), The Encountered Geotechnical Problems at Tunnel of Otogar - Kirazli Which Excavating with Tunnel Boring Machine Using, Istanbul University, Institute of Sciences, Master's Thesis.
  33. Zhang, J., Tang, W.H., Zhang, L.M. and Huang, H.W. (2012), "Characterising geotechnical model uncertainty by hybrid Markov Chain Monte Carlo simulation", Comput. Geotech., 43, 26-36. https://doi.org/10.1016/j.compgeo.2012.02.002.
  34. Zhou, M., Shadabfar, M., Xue, Y., Zhang, Y. and Huang, H. (2021), "Probabilistic analysis of tunnel roof deflection under sequential excavation using ANN-Based Monte Carlo Simulation and ssimplified reliability approach", ASCE-ASME J. Risk Uncertain. Eng. Syst. Part A: Civil Eng., 7(4). https://doi.org/10.1061/AJRUA6.0001170.
  35. Zhou, X., Lu, D., Zhang, Y., Du, X. and Rabczuk, T. (2022), "An open-source unconstrained stress updating algorithm for the modified Cam-clay model", Comput. Method. Appl. M., 390, 114356. https://doi.org/10.1016/j.cma.2021.114356.
  36. Zumrawi, M. (2014), "Effects of inadequate geotechnical investigation on civil engineering projects", Int. J. Sci. Res.