Stability Analysis of a Tunnel in Hydrostatic Conditions Using Different Reliability Methods in @Risk and RT Software

Document Type : پژوهشی

Authors

• Hadi Fattahi ¹
• Fateme Jiryaee ²

¹ Mechanics Engineering, Department of Earth Sciences Engineering, Arak University of Technology, Arak, Iran.

² Department of Earth Sciences Engineering, Arak University of Technology, Arak, Iran.

10.22067/civil.v33i2.85969

Abstract

One way to stability analysis of tunnel is to examine the convergence and radius of the plastic zone around it. On the other hand, uncertainties in the design parameters lead to the evaluation of the safety of structures using probabilistic and reliability methods. In this research was presented a methodology for stability analysis of two tunnels (under different conditions), by Hock-Brown and Mohr-Coulomb theory using robust reliability methods including: first order method (FORM), second order method (SORM) and Monte Carlo simulation in new RT and @Risk software. After probabilistic modeling in these two software, the results of the analysis show that the second order reliability method is more accurate than the first order method in more complex limit state functions. Also, at high values ​​of failure probability, first order and Monte Carlo simulation methods provide more realistic outputs and at low values ​​of failure probability, Monte Carlo simulation method is not recommended.In addition, the tunnel without pressure has a high probability of failure (24%), so that providing a support with 0.12 MPa pressure is efficient.

Keywords

• Reliability
• RT software
• @Risk software
• Tunnel stability analysis

---

References

1. Fattahi, H. and Babanouri, N., "Applying optimized support vector regression models for prediction of tunnel boring machine performance", Geotechnical and Geological Engineering, Vol. 35, No. 5, pp. 2205-2217, (2017).

2.   Fattahi, H. and Moradi, A., "Risk Assessment and Estimation of TBM Penetration Rate Using RES-Based Model", Geotechnical and Geological Engineering, Vol. 35, No. 1, pp. 365–376, (2017).

3.   Fattahi, H., "Adaptive neuro fuzzy inference system based on fuzzy C–means clustering algorithm, a technique for estimation of TBM peneteration rate", Int. J. Optim. Civil Eng, Vol. 6, No. 2, pp. 159-171, (2016).

4.   Fattahi, H., Varmazyari, Z., and Babanouri, N., "Feasibility of Monte Carlo simulation for predicting deformation modulus of rock mass", Tunnelling and Underground Space Technology, Vol. 89,  pp. 151-156, (2019).

5.   Fattahi, H., et al., "Hybrid Monte Carlo simulation and ANFIS-subtractive clustering method for reliability analysis of the excavation damaged zone in underground spaces", Computers and Geotechnics, Vol. 54, pp. 210-221, (2013).

6.   Low, B. K. and Phoon, K.-K., "Reliability-based design and its complementary role to Eurocode 7 design approach", Computers and Geotechnics, Vol. 65, pp. 30-44, (2015).

7.   Fenton, G. A., Naghibi, F., and Griffiths, D., "On a unified theory for reliability-based geotechnical design", Computers and Geotechnics, Vol. 78, pp. 110-122, (2016).

8.   Cai, J.-S., et al., "Effect of spatial variability of shear strength on reliability of infinite slopes using analytical approach", Computers and Geotechnics, Vol. 81, pp. 77-86, (2017).

9.   Li, Y., et al., "Penalty function-based method for obtaining a reliability indicator of gravity dam stability", Computers and Geotechnics, Vol. 81, pp. 19-25, (2017).

10. Metya, S., et al., "System reliability analysis of soil slopes with general slip surfaces using multivariate adaptive regression splines", Computers and Geotechnics, Vol. 87, pp. 212-228, (2017).

11. Zhang, J., et al., "Efficient response surface method for practical geotechnical reliability analysis", Computers and Geotechnics, Vol. 69, pp. 496-505, (2015).

12. Liu, L.-L., Cheng, Y.-M., and Zhang, S.-H., "Conditional random field reliability analysis of a cohesion-frictional slope", Computers and Geotechnics, Vol. 82, pp. 173-186, (2017).

13. Huang, J., et al., "Updating reliability of single piles and pile groups by load tests", Computers and Geotechnics, Vol. 73, pp. 221-230, (2016).

14. Sun, Y. and Li, X., "A probabilistic approach for assessing failure risk of cutting tools in underground excavation", Tunnelling and Underground Space Technology, Vol. 70, pp. 299-308, (2017).

15. Tee, K. F., et al., "Reliability based life cycle cost optimization for underground pipeline networks", Tunnelling and Underground Space Technology, Vol. 43, No. pp. 32-40, (2014).

16. Wang, Q., Fang, H., and Shen, L., "Reliability analysis of tunnels using a metamodeling technique based on augmented radial basis functions", Tunnelling and Underground Space Technology, Vol. 56, pp. 45-53, (2016).

17. Li, H.-Z. and Low, B. K., "Reliability analysis of circular tunnel under hydrostatic stress field", Computers and Geotechnics, Vol. 37, No. 1-2, pp. 50-58, (2010).

18. Mohammadi, S., Naseri, F., and Alipoor, S., "Development of artificial neural networks and multiple regression models for the NATM tunnelling-induced settlement in Niayesh subway tunnel, Tehran", Bulletin of Engineering Geology and the Environment, Vol. 74, No. 3, pp. 827-843, (2015).

19. Lü, Q. and Low, B. K., "Probabilistic analysis of underground rock excavations using response surface method and SORM", Computers and Geotechnics, Vol. 38, No. 8, pp. 1008-1021, (2011).

20. Goh, A. T. C. and Zhang, W., "Reliability assessment of stability of underground rock caverns", International Journal of Rock Mechanics and Mining Sciences, Vol. 55, pp. 157-163, (2012).

21. Song, L., et al., "Reliability analysis of underground excavation in elastic-strain-softening rock mass", Tunnelling and Underground Space Technology, Vol. 60, pp. 66-79, (2016).