Geomechanics and Engineering

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

In-situ monitoring and reliability analysis of an embankment slope with soil variability

  • Bai, Tao (School of Civil Engineering and Architecture, Wuhan Institute of Technology) ;
  • Yang, Han (School of Civil Engineering and Architecture, Wuhan Institute of Technology) ;
  • Chen, Xiaobing (School of Transportation, Southeast University) ;
  • Zhang, Shoucheng (Wuhan Municipal Engineering Design & Research Institute Co., Ltd.) ;
  • Jin, Yuanshang (Liaoning Zhixin Highway Engineering Technology Consulting Corporation)
  • Received : 2020.07.08
  • Accepted : 2020.10.25
  • Published : 2020.11.10
⟨ Previous Next ⟩

Abstract

This paper presents an efficient method utilizing user-defined computer functional codes to determine the reliability of an embankment slope with spatially varying soil properties in real time. The soils' mechanical properties varied with the soil layers that had different degrees of compaction and moisture content levels. The Latin Hypercube Sampling (LHS) for the degree of compaction and Kriging simulation of moisture content variation were adopted and programmed to predict their spatial distributions, respectively, that were subsequently used to characterize the spatial distribution of the soil shear strengths. The shear strength parameters were then integrated into the Geostudio command file to determine the safety factor of the embankment slope. An explicit metamodal for the performance function, using the Kriging method, was established and coded to efficiently compute the failure probability of slope with varying moisture contents. Sensitivity analysis showed that the proposed method significantly reduced the computational time compared to Monte Carlo simulation. About 300 times LHS Geostudio computations were needed to optimize precision and efficiency in determining the failure probability. The results also revealed that an embankment slope is prone to high failure risk if the degree of compaction is low and the moisture content is high.

Keywords

Acknowledgement

This work was jointly supported by the Science and Technology Project of Wuhan Municipal Engineering Design & Research Institute Co., Ltd. (grant number H00446), Plan of Outstanding Young and Middle-aged Scientific and Technological Innovation Team in Universities of Hubei Province (grant number T2020010), Natural Science Foundation of Hubei Province (grant number 2020CFB567), and the Science and Technology Project of Ministry of Transport of the People's Republic of China. The authors also greatly appreciates and gratefully acknowledges the sponsors' funding support.

References

  1. Bai, T., Hu, X.D. and Gu, F. (2019), "Practice of searching a noncircular critical slip surface in a slope with soil variability", Int. J. Geomech., 19(3), 04018199 1-13. https://doi.org/10.1061/(ASCE)gm.1943-5622.0001350.
  2. Bai, T., Qiu, T., Huang, X.M. and Li, C. (2014), "Locating global critical slip surface using the Morgenstern-Price method and optimization technique", Int. J. Geomech., 4(2), 319-325. https://doi.org/10.1061/(ASCE)gm.1943-5622.0000312.
  3. Chenari, R.J., Fatahi, B., Ghoreishi, M. and Taleb, A. (2019), "Physical and numerical modelling of the inherent variability of shear strength in soil mechanics", Geomech. Eng., 17(1), 31-45. https://doi.org/10.12989/gae.2019.17.1.031.
  4. Cho, S.E. (2012), "Probabilistic analysis of seepage that considers the spatial variability of permeability for an embankment on soil foundation", Eng. Geol., 133(3), 30-39. https://doi.org/10.1016/j.enggeo.2012.02.013.
  5. Cioppa, T. and Lucas, T. (2007), "Efficient nearly orthogonal and space-filling Latin hypercubes", Technometrics, 49(1), 45-55. https://doi.org/10.1198/004017006000000453.
  6. Deng, W.D. and Tang, X.Y. (1994), "Statistical analysis of physical parameters of embankment fill", J. Chongqing Jiaotong Inst., 13(2), 69-76 (in Chinese).
  7. Echard, B., Gayton, N. and Lemaire, M. (2011), "AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation", Struct. Saf., 33(2), 145-154. https://doi.org/10.1016/j.strusafe.2011.01.002.
  8. El-Ramly, H., Morgenstern, N.R. and Cruden, D.M. (2002), "Probabilistic slope stability analysis for practice", Can. Geotech. J., 39(3), 665-683. https://doi.org/10.1139/t02-034.
  9. Hassan, A.M. and Wolff, T.F. (1992), "Search algorithm for minimum reliability index of earth slope", J. Geotech. Geoenviron. Eng., 125(4), 301-308. https://doi.org/10.1061/(ASCE)1090-0241(1999)125:4(301).
  10. Huntington, D. and Lyrintzis, C. (1998), "Improvements to and limitations of Latin hypercube sampling", Probabilist. Eng. Mech., 13(4), 245-253. https://doi.org/10.1016/S0266-8920(97)00013-1.
  11. Ji, J., Zhang, C.S., Gao, Y.F. and Kodikara, J. (2018), "Effect of 2D spatial variability on slope reliability: A simplified FORM analysis", Geosci. Front., 9(6), 1631-1638. https://doi.org/10.1016/j.gsf.2017.08.004.
  12. Jones, D.R., Schonlau, M. and Welch, W.J. (1998), "Efficient global optimization of expensive black-box functions", J. Global Optim., 13(4), 455-492. https://doi.org/10.1023/A:1008306431147.
  13. Kim, J.M., Lee, S., Park, J.Y., Kihm, J.H. and Park, S. (2020), "A set of failure variables for analyzing stability of slopes and tunnels", Geomech. Eng., 20(3), 175-189. https://doi.org/10.12989/gae.2020.20.3.175.
  14. Kouritzin, M.A., Le, K. and Sezer, D. (2016), "Laws of large numbers for supercritical branching Gaussian processes", Stoch. Proc. Appl., 129(9), 3463-3498. https://doi.org/10.1016/j.spa.2018.09.011.
  15. Li, L., Lin, C. and Zhang, Z.G. (2017), "Utilization of shale-clay mixtures as a landfill liner material to retain heavy metals", Mater. Design, 114, 73-82. https://doi.org/10.1016/j.matdes.2016.10.046.
  16. Liu, K., Vardon, P.J., Hicks, M.A. and Arnold, P. (2017b), "Combined effect of hysteresis and heterogeneity on the stability of an embankment under transient seepage", Eng. Geol., 219(9), 140-150. https://doi.org/10.1016/j.enggeo.2016.11.011.
  17. Liu, L.L., Cheng, Y.M., Wang, X.M., Zhang, S.H. and Wu, Z.H. (2017a), "System reliability analysis and risk assessment of a layered slope in spatially variable soils considering stratigraphic boundary uncertainty", Comput. Geotech., 89, 213-225. https://doi.org/10.1016/j.compgeo.2017.05.014.
  18. Lophaven, S. N. and Sondergaard, J. (2002), DACE-A MATLAB Kriging Toolbox Version 2.0, Technical Report IMM-TR-2002-12, 1-25.
  19. McKay, M.D., Conover, W.J. and Beckman, R.J. (1979), "A comparison of three methods for selecting values of input variables in the analysis of output from a computer code", Technometrics, 21(2), 239-245. https://doi.org/10.1080/00401706.1979.10489755.
  20. Modoni, G., Albano, M., Salvatore, E. and Koseki, J. (2018), "Effects of compaction on the seismic performance of embankments built with gravel", Soil Dyn. Earthq. Eng., 106, 231-242. https://doi.org/10.1016/j.soildyn.2018.01.005.
  21. Olsson, A.M.J. and Sandberg, G.E. (2002), "Latin hypercube sampling for stochastic finite element analysis", J. Eng. Mech., 128(1), 121-125. https://doi.org/10.1061/(ASCE)0733-9399(2002)128:1(121).
  22. Phoon, K.K. and Kulhawy, F.H. (1999), "Characterization of geotechnical variability", Can. Geotech. J., 36(4), 612-624. https://doi.org/10.1139/t99-038.
  23. Saseendran, R. and Dodagoudar, G.R. (2020), "Reliability analysis of slopes stabilised with piles using response surface method" Geomech. Eng., 21(6), 513-525. https://doi.org/10.12989/gae.2020.21.6.513.
  24. Su, Y.H. and Yang, H.B. (2012), "Reliability algorithm of slope stability based on Kriging metamodel", Chin. J. Appl. Mech., 29(6), 705-710 (in Chinese).
  25. Zhao, L.Y., Huang, Y., Xiong, M. and Ye, G.B. (2020), "Reliability and risk assessment for rainfall-induced slope failure in spatially variable soils", Geomech. Eng., 22(3), 207-217. https://doi.org/10.12989/gae.2020.22.3.207.