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KSCE Journal of Civil Engineering

, Volume 23, Issue 12, pp 5032–5040 | Cite as

Probabilistic Risk Assessment of unsaturated Slope Failure Considering Spatial Variability of Hydraulic Parameters

  • Lin Wang
  • Chongzhi Wu
  • Yongqin Li
  • Hanlong Liu
  • Wengang ZhangEmail author
  • Xiang Chen
Geotechnical Engineering
  • 45 Downloads

Abstract

Probabilistic risk assessment of slope failure evaluates the slope safety in a quantitative manner, which considers the failure probability and failure consequence simultaneously. However, risk assessment of unsaturated slope accounting for spatially variable soil-water characteristic curve (SWCC) model parameter and saturated hydraulic conductivity has been rarely reported. A probabilistic risk assessment approach is proposed in current study for rationally quantifying the unsaturated slope failure risk with the aid of Monte Carlo (MC) simulation. The SEEP/W and SLOPE/W modules contained in Geostudio software are applied to carry out deterministic analysis, where factor of safety (FS) of the unsaturated slope is calculated by Morgenstern–Price method. The spatially variable hydraulic parameters are characterized by their respective random fields that are transferred from the random void ratio field in this study, rather than generating them separately. The proposed approach is subsequently employed to an unsaturated slope example for exploring the influences of spatially variable void ratio. Results show that the unsaturated slope failure risk is considerably affected by the spatially variable void ratio, and the single exponential autocorrelation function (ACF) popularized in geotechnical engineering tends to underestimate the failure risk in the unsaturated slope risk assessment.

Keywords

unsaturated slope risk assessment spatial variability hydraulic parameters random field 

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Notes

Acknowledgements

This work was supported by the Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering (No. 2019018) and Chongqing Engineering Research Center of Disaster Prevention & Control for Banks and Structures in Three Gorges Reservoir Area (Nos. SXAPGC18ZD01 and SXAPGC18YB03). The financial support is gratefully acknowledged.

References

  1. Ali, A., Huang, J. S., Lyamin, A. V., Sloan, S. W., Griffiths, D. V., Cassidy, M. J., and Li, J. H. (2014). “Simplified quantitative risk assessment of rainfall-induced landslides modelled by infinite slopes.” Engineering Geology, Vol. 179, pp. 102–116, DOI:  https://doi.org/10.1016/j.enggeo.2014.06.024.CrossRefGoogle Scholar
  2. Cao, Z. J. and Wang, Y. (2014). “Bayesian model comparison and selection of spatial correlation functions for soil parameters.” Structural Safety, Vol. 49, pp. 10–17, DOI:  https://doi.org/10.1016/j.strusafe.2013.06.003.CrossRefGoogle Scholar
  3. Carsel, R. F. and Parrish, R. S. (1988). “Developing joint probability distributions of soil water retention characteristics.” Water Resources Research, Vol. 24, No. 5, pp. 755–769, DOI:  https://doi.org/10.1029/WR024i005p00755.CrossRefGoogle Scholar
  4. Chen, F. Y., Wang, L., and Zhang, W. G. (2019). “Reliability assessment on stability of tunnelling perpendicularly beneath an existing tunnel considering spatial variabilities of rock mass properties.” Tunnelling and Underground Space Technology, Vol. 88, pp. 276–289, DOI:  https://doi.org/10.1016/j.tust.2019.03.013.CrossRefGoogle Scholar
  5. Cheng, H. Z., Chen, J., Chen, R. P., Chen, G. L., and Zhong, Y. (2018). “Risk assessment of slope failure considering the variability in soil properties.” Computers and Geotechnics, Vol. 103, pp. 61–72, DOI:  https://doi.org/10.1016/j.compgeo.2018.07.006.CrossRefGoogle Scholar
  6. Chiu, C. F., Yan, W. M., and Yuen, K. V. (2012). “Reliability analysis of soil–water characteristics curve and its application to slope stability analysis.” Engineering Geology, Vols. 135-136, pp. 83–91, DOI:  https://doi.org/10.1016/j.enggeo.2012.03.004.CrossRefGoogle Scholar
  7. Fenton, G. A. and Griffiths, D. V. (2008). Risk assessment in geotechnical engineering, John Wiley & Sons, New York, NY, USA.CrossRefGoogle Scholar
  8. Fredlund, D. G., Rahardjo, H., and Fredlund, M. D. (2012). Unsaturated soil mechanics in engineering practice, John Wiley & Sons, Hoboken, NJ, USA.CrossRefGoogle Scholar
  9. GEO-SLOPE (2012). “Geostudio.” GEO-SLOPE International, Ltd., https://www.geoslope.com, [Accessed on December 5, 2014].
  10. Huang, J., Lyamin, A. V., Griffiths, D. V., Krabbenhoft, K., and Sloan, S. W. (2013). “Quantitative risk assessment of landslide by limit analysis and random fields.” Computers and Geotechnics, Vol. 53, pp. 60–67, DOI:  https://doi.org/10.1016/j.compgeo.2013.04.009.CrossRefGoogle Scholar
  11. Jha, S. K. and Ching, J.Y. (2012). “Simulating spatial averages of stationary random field using fourier series method.” Journal of Engineering Mechanics, Vol. 139, No. 5, pp. 594–605, DOI:  https://doi.org/10.1061/(ASCE)EM.1943-7889.0000517.CrossRefGoogle Scholar
  12. Jiang, S. H., Li, D. Q., Zhang, L. M., and Zhou, C. B. (2015). “Slope reliability analysis considering spatially variable shear strength parameters using a non-intrusive stochastic finite element method.” Engineering Geology, Vol. 168, pp. 120–128, DOI:  https://doi.org/10.1016/j.enggeo.2013.11.006.CrossRefGoogle Scholar
  13. Le, T. M. H., Gallipoli, D., Sanchez, M., and Wheeler, S. (2013). “Rainfall-induced differential settlements of foundations on heterogeneous unsaturated soils.” Géotechnique, Vol. 63, No. 15, pp. 1346–1355, DOI:  https://doi.org/10.1680/geot.12.P.181.CrossRefGoogle Scholar
  14. Le, T. M. H., Gallipoli, D., Sánchez, M., and Wheeler, S. (2015). “Stability and failure mass of unsaturated heterogeneous slopes.” Canadian Geotechnical Journal, Vol. 52, No. 11, pp. 1747–1761, DOI:  https://doi.org/10.1139/cgj-2014-0190.CrossRefGoogle Scholar
  15. Leong, E. C. and Rahardjo, H. (1997). “Permeability functions for unsaturated soils.” Journal of Geotechnical and Geoenvironmental Engineering, Vol. 123, No. 12, pp. 1118–1126, DOI:  https://doi.org/10.1061/(ASCE)1090-0241(1997)123:12(1118).CrossRefGoogle Scholar
  16. Li, L. and Chu, X. S. (2016). “Risk assessment of slope failure by representative slip surfaces and response surface function.” KSCE Journal of Civil Engineering, Vol. 20, No. 5, pp. 1783–1792, DOI:  https://doi.org/10.1007/s12205-015-2243-6.CrossRefGoogle Scholar
  17. Li, D. Q., Jiang, S. H., Cao, Z. J., Zhou, W., Zhou, C. B., and Zhang, L. M. (2015). “A multiple response-surface method for slope reliability analysis considering spatial variability of soil properties.” EngineeringGeology, Vol. 187, pp. 60–72, DOI:  https://doi.org/10.1016/j.enggeo.2014.12.003.Google Scholar
  18. Li, L. B., Phoon, K. K., and Quek, S. T. (2007). “Comparison between Karhunen–Loève expansion and translation-based simulation of non-Gaussian processes.” Computers & Structures, Vol. 85, pp. 264–276, DOI:  https://doi.org/10.1016/j.compstruc.2006.10.010.MathSciNetCrossRefGoogle Scholar
  19. Li, D. Q., Wang, L., Cao, Z. J., and Qi, X. H. (2019a). “Reliability analysis of unsaturated slope stability considering SWCC model selection and parameter uncertainties.” Engineering Geology, Vol. 260, p. 105207, DOI:  https://doi.org/10.1016/j.enggeo.2019.105207.CrossRefGoogle Scholar
  20. Li, D. Q., Xiao, T., Cao, Z. J., Zhou, C. B., and Zhang, L. M. (2016). “Enhancement of random finite element method in reliability analysis and risk assessment of soil slopes using subset simulation.” Landslides, Vol. 13, No. 2, pp. 293–303, DOI:  https://doi.org/10.1007/s10346-015-0569-2.CrossRefGoogle Scholar
  21. Li, D., Q., Yang, Z. Y., Cao, Z. J., and Zhang, L. M. (2019b). “Area failure probability method for slope system failure risk assessment.” Computers and Geotechnics, Vol. 107, pp. 36–44, DOI:  https://doi.org/10.1016/j.compgeo.2018.11.017.CrossRefGoogle Scholar
  22. Liu, L. L., Cheng, Y. M., Wang, X. M., Zhang, S. H, and Wu, Z. H. (2017). “System reliability analysis and risk assessment of a layered slope in spatially variable soils considering stratigraphic boundary uncertainty.” Computers and Geotechnics, Vol. 89, pp. 213-225, DOI:  https://doi.org/10.1016/j.compgeo.2017.05.014.CrossRefGoogle Scholar
  23. Liu, L. L., Zhang, S. H., Cheng, Y. M., and Liang, L. (2019). “Advanced reliability analysis of slopes in spatially variable soils using multivariate adaptive regression splines.” Geoscience Frontiers, Vol. 10, No. 2, pp. 671–682, DOI:  https://doi.org/10.1016/j.gsf.2018.03.013.CrossRefGoogle Scholar
  24. Low, B. K. (2007). “Reliability analysis of rock slopes involving correlated nonnormals.” International Journal of Rock Mechanics and Mining Sciences, Vol. 44, No. 6, pp. 922–935, DOI:  https://doi.org/10.1016/j.ijrmms.2007.02.008.CrossRefGoogle Scholar
  25. Lu, N. and Likos, W. J. (2004). Unsaturated soil mechanics, John Wiley & Sons, Hoboken, NJ, USA.Google Scholar
  26. Miao, F. S., Wu, Y. P., Xie, Y. H., Yu, F., and Peng, L. J. (2017). “Research on progressive failure process of Baishuihe landslide based on Monte Carlo model.” Stochastic Environmental Research and Risk Assessment, Vol. 31, No. 7, pp. 1683–1696, DOI:  https://doi.org/10.1007/s00477-016-1224-8.CrossRefGoogle Scholar
  27. Mualem, Y. (1976). “A new model for predicting the hydraulic conductivity of unsaturated porous media.” Water Resources Research, Vol. 12, No. 3, pp. 513–522, DOI:  https://doi.org/10.1029/WR012i003p00513.CrossRefGoogle Scholar
  28. Phoon, K. K. and Ching, J. Y. (2015). Risk and reliability in geotechnical engineering, CRC Press, New York, NY, USA.Google Scholar
  29. Phoon, K. K. and Kulhawy, F. H. (1999). “Characterization of geotechnical variability.” Canadian Geotechnical Journal, Vol. 36, No. 4, pp. 612–624, DOI:  https://doi.org/10.1139/t99-038.CrossRefGoogle Scholar
  30. Phoon, K. K., Santoso, A., and Quek, S. T. (2010). “Probabilistic analysis of soil-water characteristic curves.” Journal of Geotechnical and Geoenvironmental Engineering, Vol. 136, No. 3, pp. 445–455, DOI:  https://doi.org/10.1061/(ASCE)GT.1943-5606.0000222.CrossRefGoogle Scholar
  31. Santoso, A. M., Phoon, K. K., and Quek, S. T. (2011). “Effects of soil spatial variability on rainfall-induced landslides.” Computers and Structures, Vol. 89, Nos. 11-12, pp. 893–900, DOI:  https://doi.org/10.1016/j.compstruc.2011.02.016.CrossRefGoogle Scholar
  32. van Genuchten, M. T. (1980). “A closed-form equation for predicting the hydraulic conductivity of unsaturated soils.” Soil Science Society of America Journal, Vol. 44, No. 5, pp. 892–898, DOI:  https://doi.org/10.2136/sssaj1980.03615995004400050002x.CrossRefGoogle Scholar
  33. Wang, L., Cao, Z. J., Li, D. Q., Phoon, K. K., and Au, S. K. (2018). “Determination of site-specific soil-water characteristic curve from a limited number of test data – A Bayesian perspective.” Geoscience Frontiers, Vol. 9, No. 6, pp. 1665–1677, DOI:  https://doi.org/10.1016/j.gsf.2017.10.014.CrossRefGoogle Scholar
  34. Wang, L., Wu, C. Z., and Zhang, W. G. (2019a). “Reliability analysis of unsaturated slope stability considering spatial variability in hydraulic parameters.” IOP Conference Series: Earth and Environmental Science, Vol. 304, No. 3, p. 032047, DOI:  https://doi.org/10.1088/1755-1315/304/3/032047.Google Scholar
  35. Wang, L., Zhang, W. G., and Chen, F. Y. (2019b). “Bayesian approach for predicting soil-water characteristic curve from particle-size distribution data.” Energies, Vol. 12, p. 2992, DOI:  https://doi.org/10.3390/en12152992.CrossRefGoogle Scholar
  36. Zhai, Q. and Rahardjo, H. (2015). “Estimation of permeability function from the soil-water characteristic curve.” Engineering Geology, Vol. 199, pp. 148–156, DOI:  https://doi.org/10.1016/j.enggeo.2015.11.001.CrossRefGoogle Scholar
  37. Zhang, W. G. and Goh, A. T. C. (2013). “Multivariate adaptive regression splines for analysis of geotechnical engineering systems.” Computers and Geotechnics, Vol. 48, pp. 82–95, DOI:  https://doi.org/10.1016/j.compgeo.2012.09.016.CrossRefGoogle Scholar
  38. Zhang, W. G. and Goh, A. T. C. (2016). “Multivariate adaptive regression splines and neural network models for prediction of pile drivability.”Geoscience Frontiers, Vol. 7, No. 1, pp. 45–52, DOI:  https://doi.org/10.1016/j.gsf.2014.10.003.CrossRefGoogle Scholar
  39. Zhang, J. and Huang, H. W. (2016). “Risk assessment of slope failure considering multiple slip surfaces.” Computers and Geotechnics, Vol. 74, pp. 188–195, DOI:  https://doi.org/10.1016/j.compgeo.2016.01.011.CrossRefGoogle Scholar
  40. Zhang, J., Huang, H. W., Juang, C. H., and Su, W. W. (2014). “Geotechnical reliability analysis with limited data: Consideration of model selection uncertainty.” Engineering Geology, Vol. 181, pp. 27–37, DOI:  https://doi.org/10.1016/j.enggeo.2014.08.002.CrossRefGoogle Scholar
  41. Zhou, C., Yin, K. L., Cao, Y., Intrieri, E., Ahmed, B., and Catani, F. (2018). “Displacement prediction of step-like landslide by applying a novel kernel extreme learning machine method.” Landslides, Vol. 15, pp. 2211–2225, DOI:  https://doi.org/10.1007/s10346-018-1022-0.CrossRefGoogle Scholar
  42. Zhu, X., Xu, Q., Tang, M. G., Nie, W., Ma, S. Q., and Xu, Z. P. (2017). “Comparison of two optimized machine learning models for predicting displacement of rainfall-induced landslide: A case study in Sichuan.” Engineering Geology, Vol. 218, pp. 213–222, DOI:  https://doi.org/10.1016/j.enggeo.2017.01.022.CrossRefGoogle Scholar

Copyright information

© Korean Society of Civil Engineers 2019

Authors and Affiliations

  1. 1.School of Civil EngineeringChongqing UniversityChongqingChina
  2. 2.School of Civil EngineeringChongqing Three Gorges UniversityChongqingChina

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