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Dynamic process simulation of construction solid waste (CSW) landfill landslide based on SPH considering dilatancy effects

  • Heng Liang
  • Siming HeEmail author
  • Xiaoqin Lei
  • Yuzhang Bi
  • Wei Liu
  • Chaojun Ouyang
Original Paper

Abstract

Construction solid waste (CSW) landfill landslides, such as the Guangming New District landslide, which occurred in Shenzhen (hereafter the Shenzhen landslide), occur when the material is loose and saturated. They usually exhibit characteristics such as abrupt failure and whole collapse. During the propagation of landslides, dilatation behavior plays an important role in causing liquefaction, resulting in high velocity and exceptionally long run-out dynamics. We propose a dynamic model for describing fluidized CSW landslides by integrating the dilatancy model into smoothed particle hydrodynamics (SPH). The dilatancy model implies that the occurrence of dilation or the contraction of the granular-fluid mixture depends on the initial solid volume fraction. The dynamic model is used to simulate the Shenzhen landslide, and special attention is paid to the effects of different initial solid volumes on the mobility of the CSW landslide. The results show that when the solid volume fraction is higher than the critical value, contraction occurs, the excess pore water pressure increases, and the basal friction resistance is reduced. CSW landslide mobility is based on the initial solid volume fraction (or initial void ratio) of the granular-fluid mixture; a slight change in the initial volume fraction significantly affects the mobility of the CSW landfill landslide.

Keywords

CSW landfill landslide Dilatancy effects Dynamic processes SPH 

List of symbols

\(b\)

Proportionality coefficient

\(c\)

Cohesion

\(\dot{e}_{ij}\)

Deviatoric strain-rate tensor

\(G\)

Shear modulus of soil skeleton

\(K_{1}\)

Bulk modulus of soil skeleton

\(K_{\text{m}}\)

Bulk modulus of solid particles

\(K_{\text{s}}\)

Bulk modulus of solid particles

\(K_{\text{w}}\)

Bulk modulus of water

\(K_{t}\)

Total bulk modulus

LM

Maximum source area material migration displacement

\(m_{0}\)

Initial solid volume fraction

\(m_{\text{s}}\)

Current solid volume fraction

\(m_{\text{w}}\)

Current water volume fraction

\(m_{\text{a}}\)

Current air volume fraction

\(m_{\text{eq}}\)

Equilibrium solid volume fraction

\(m_{\text{crit}}\)

Lithostatic critical-state solid volume fraction

N

Generalized dimensionless parameter

\(p_{0}^{\text{a}}\)

Standard atmospheric pressure

\(\Delta p^{\text{a}}\)

Pressure increment

\(s_{ij}^{\text{N}}\)

New second invariant of deviatoric stress tensor

\(u_{\text{w}}\)

Pore water pressure

\(u_{\text{a}}\)

Pore air pressure

\(u_{\text{s}}\)

Matric suction

\(u_{\text{f}}\)

Pore fluid pressure

\(\Delta u_{\text{f}}^{\text{e}}\)

Excess pore fluid pressure increment

\(u_{\text{t}}^{\text{C}}\)

Corrected values of pore fluid pressure

\(u_{\text{f}}^{\text{N}}\)

New pore fluid pressure

v0

Total volume

\(\dot{\gamma }\)

Shear strain-rate

\(\Delta \gamma\)

Shear strain increment

\(\delta_{ij}\)

Kronecker’s delta

\(\mu\)

Effective shear viscosity of pore fluid

\(\rho\)

Current mixture bulk density

\(\rho_{0}\)

Initial mixture bulk density

\(\rho_{\text{s}}\)

Soil grain density

\(\rho_{\text{w}}\)

Initial fluid phase density

\(\varepsilon_{\text{v}}\)

Volume strain of solid skeleton

\(\Delta \varepsilon_{\text{v}}^{\text{e}}\)

Volume strain increment

\(\xi\)

Calibration constant

\(\zeta\)

Characteristic grain diameter

\(\sigma_{0}\)

Reference mean stress

\(\sigma_{\text{e}}\)

Mean effective stress

\(\sigma_{\text{t}}\)

Mean total stress

\(\sigma_{\text{t}}^{\text{C}}\)

Corrected values of total mean stress

\(\sigma_{\text{e}}^{\text{N}}\)

New effective stress

\(\sigma_{ij}^{{\prime }}\)

Net stress tensor acting on solid skeleton

\(\sigma_{ij}^{\text{N}}\)

New total stress

\(\sigma_{\text{t}}^{\text{N}}\)

New total mean stress

\(\tau_{\hbox{min} }\)

Minimum shear strength

\(\tau_{\text{s}}\)

Shear stress

\(\tau^{\text{N}}\)

New second deviatoric shear stress

\(\phi\)

Internal friction angle

\(\psi\)

Dilatancy angle

Notes

Acknowledgments

This work was supported as a joint research project by NSFC-ICIMOD (Grant no. 41661144041) and the Science and Technology Department of Sichuan Province of China (Grant no. 2016SZ0067).

References

  1. Anderson JD, Wendt J (1995) Computational fluid dynamics, vol 206. McGraw-Hill, New YorkGoogle Scholar
  2. Bolton MD (1984) The strength and dilatancy of sands. Cambridge University Engineering DepartmentGoogle Scholar
  3. Bouchut F, Fernández-Nieto ED, Mangeney A, Narbona-Reina G (2016) A two-phase two-layer model for fluidized granular flows with dilatancy effects. J Fluid Mech 801:166–221CrossRefGoogle Scholar
  4. Cascini L, Cuomo S, Pastor M, Sorbino G, Piciullo L (2012) Modeling of propagation and entrainment phenomena for landslides of the flow type: the May 1998 case study. In: Landslides and engineered slopes: protecting society through improved understanding. Proceedings of the 11th International and 2nd North American Symposium on Landslides, vol 38, pp 1723–1729, ChiricoGoogle Scholar
  5. Cascini L, Cuomo S, Pastor M, Sorbino G, Piciullo L (2014) SPH run-out modelling of channelised landslides of the flow type. Geomorphology 214:502–513CrossRefGoogle Scholar
  6. Chen CL (1987) Comprehensive review of debris flow modeling concepts in Japan. Rev Eng Geol 7:13–30CrossRefGoogle Scholar
  7. Chen WF, Mizuno E (1990) Nonlinear analysis in soil mechanics. Elsevier, Amsterdam, pp 143–150Google Scholar
  8. Cola S, Calabrò N, Pastor M (2008) Prediction of the flow-like movements of Tessina landslide by SPH model. Landslides and engineered slopes. Taylor & Francis Group, LondonGoogle Scholar
  9. Colomer-Mendoza FJ, Esteban-Altabella J, García-Darás F, Gallardo-Izquierdo A (2013) Influence of the design on slope stability in solid waste landfills. Earth Sci 2(2):31–39Google Scholar
  10. Davies TR, McSaveney MJ (2009) The role of rock fragmentation in the motion of large landslides. Eng Geol 109(1):67–79CrossRefGoogle Scholar
  11. Fung YC, Tong P (2001) Classical and computational solid mechanics, vol 1. World ScientificGoogle Scholar
  12. George DL, Iverson RM (2014) A depth-averaged debris-flow model that includes the effects of evolving dilatancy: II. Numerical predictions and experimental tests. Proc R Soc A 470(2170):20130820Google Scholar
  13. Gray JMNT, Edwards AN (2014) A depth-averaged-rheology for shallow granular free-surface flows. J Fluid Mech 755:503–534CrossRefGoogle Scholar
  14. Haddad B, Pastor M, Palacios D, Munoz-Salinas E (2010) A SPH depth integrated model for Popocatépetl 2001 lahar (Mexico): sensitivity analysis and runout simulation. Eng Geol 114:312–329CrossRefGoogle Scholar
  15. Huang Y, Dai Z (2014) Large deformation and failure simulations for geo-disasters using smoothed particle hydrodynamics method. Eng Geol 168:86–97CrossRefGoogle Scholar
  16. Huang Y, Zhang W, Mao W, Jin C (2011) Flow analysis of liquefied soils based on smoothed particle hydrodynamics. Nat Hazards 59:1547–1560CrossRefGoogle Scholar
  17. Huang Y, Zhang W, Xu Q, Xie P, Hao L (2012) Run-out analysis of flow-like landslides triggered by the Ms 8.0 2008 Wenchuan earthquake using smoothed particle hydrodynamics. Landslides 9:275–283CrossRefGoogle Scholar
  18. Huang Y, Dai Z, Zhang W, Huang M (2013) SPH-based numerical simulations of flow slides in municipal solid waste landfills. Waste Manage Res 31:256–264CrossRefGoogle Scholar
  19. Hutter K, Laloui L, Vulliet L (1999) Thermodynamically based mixture models of saturated and unsaturated soils. Mech Cohes Frict Maters 4:295–338CrossRefGoogle Scholar
  20. Ishihara K, Yasuda S, Yoshida Y (1989) Liquefaction-induced flow failure of embankments and residual strength of silty sands. Sino-Japan Joint Symposium on Improvement of Weak Ground, pp 69–80Google Scholar
  21. Iverson RM (2000) Landslide triggering by rain infiltration. Water Resour Res 36:1897–1910CrossRefGoogle Scholar
  22. Iverson RM (2003) The debris-flow rheology myth[C]. Debris Flow Hazards Mitig Mech Predict Assess 1:303–314Google Scholar
  23. Iverson RM (2005) Regulation of landslide motion by dilatancy and pore pressure feedback. J Geophys Res Earth Surf 110(F2):273–280CrossRefGoogle Scholar
  24. Iverson RM, George DL (2014) A depth-averaged debris-flow model that includes the effects of evolving dilatancy. I. Physical basis. Proc R Soc A 470(2170):20130819Google Scholar
  25. Iverson RM, George DL (2016) Modelling landslide liquefaction, mobility bifurcation and the dynamics of the 2014 Oso disaster. Géotechnique 66:175–187CrossRefGoogle Scholar
  26. Iverson RM, Reid ME, Iverson NR, LaHusen RG, Logan M, Mann JE, Brien DL (2000) Acute sensitivity of landslide rates to initial soil porosity. Science 290:513–516CrossRefGoogle Scholar
  27. Jackson R (2000) The dynamics of fluidized particles. Cambridge University PressGoogle Scholar
  28. Konrad JM (1990) Minimum undrained strength of two sands. J Geotech Eng 116:932–947CrossRefGoogle Scholar
  29. Konrad JM (1993) Undrained response of loosely compacted sands during monotonic and cyclic compression tests. Géotechnique 43:69–89CrossRefGoogle Scholar
  30. Legros F (2002) The mobility of long-runout landslides. Eng Geol 63:301–331CrossRefGoogle Scholar
  31. Liu GR, Gu YT (2005) An introduction to meshfree methods and their programming. Springer, BerlinGoogle Scholar
  32. Liu GR, Liu MB (2003) Smoothed particle hydrodynamics: a meshfree particle method. World ScientificGoogle Scholar
  33. Liu MB, Liu GR (2010) Smoothed particle hydrodynamics (SPH): an overview and recent developments. Arch Comput Methods Eng 17:25–76CrossRefGoogle Scholar
  34. Lucas A, Mangeney A, Ampuero JP (2014) Frictional velocity-weakening in landslides on Earth and on other planetary bodies. Nat Commun 5:3417CrossRefGoogle Scholar
  35. Lucy LB (1977) A numerical approach to the testing of the fission hypothesis. Astronom J 82:1013–1024CrossRefGoogle Scholar
  36. Major JJ (2000) Gravity-driven consolidation of granular slurries: implications for debris-flow deposition and deposit characteristics. J Sediment Res 70:64–83CrossRefGoogle Scholar
  37. Major JJ, Iverson RM, McTigue DF, Macias S, Fiedorowicz BK (1997) Geotechnical properties of debris-flow sediments and slurries. In: Proceedings of the 1997 1st International Conference on Debris-Flow Hazards Mitigation: Mechanics, Prediction, and Assessment, pp 249–259Google Scholar
  38. McDougall S, Hungr O (2005) Dynamic modelling of entrainment in rapid landslides. Can Geotech J 42:1437–1448CrossRefGoogle Scholar
  39. Ochiai H, Okada Y, Furuya G, Okura Y, Matsui T, Sammori T, Terajima T, Sassa K (2004) A fluidized landslide on a natural slope by artificial rainfall. Landslides 1:211–219CrossRefGoogle Scholar
  40. Ouyang C, Zhou K, Xu Q, Yin J, Peng D, Wang D, Li W (2016) Dynamic analysis and numerical modeling of the 2015 catastrophic landslide of the construction waste landfill at Guangming, Shenzhen, China. Landslides 1–14Google Scholar
  41. Pailha M, Pouliquen O (2009) A two-phase flow description of the initiation of underwater granular avalanches. J Fluid Mech 633:115–135CrossRefGoogle Scholar
  42. Pastor M, Blanc T, Pastor MJ (2009a) A depth-integrated viscoplastic model for dilatant saturated cohesive-frictional fluidized mixtures: application to fast catastrophic landslides. J Nonnewton Fluid Mech 158:142–153CrossRefGoogle Scholar
  43. Pastor M, Haddad B, Sorbino G, Cuomo S, Drempetic V (2009b) A depth-integrated, coupled SPH model for flow-like landslides and related phenomena. Int J Num Anal Meth Geomech 33:143–172CrossRefGoogle Scholar
  44. Pastor M, Blanc T, Haddad B, Petrone S, Morles MS, Drempetic V, Issler D, Crosta GB, Cascini L, Sorbino G, Cuomo S (2014) Application of a SPH depth-integrated model to landslide run-out analysis. Landslides 11:793–812CrossRefGoogle Scholar
  45. Sheng D, Fredlund DG, Gens A (2008) A new modelling approach for unsaturated soils using independent stress variables. Can Geotech J 45:511–534CrossRefGoogle Scholar
  46. Sheng LT, Tai YC, Kuo CY, Hsiau SS (2013) A two-phase model for dry density-varying granular flows. Adv Powder Technol 24:132–142CrossRefGoogle Scholar
  47. Uzuoka R, Yashima A, Kawakami T, Konrad JM (1998) Fluid dynamics based prediction of liquefaction induced lateral spreading. Comput Geotech 22:243–282CrossRefGoogle Scholar
  48. Violeau D (2012) Fluid mechanics and the SPH method: theory and applications. Oxford University Press, OxfordGoogle Scholar
  49. Wang G, Sassa K (2003) Pore-pressure generation and movement of rainfall-induced landslides: effects of grain size and fine-particle content. Eng Geol 69:109–125CrossRefGoogle Scholar
  50. Yin Y, Li B, Wang W, Zhan L, Xue Q, Gao Y, Zhang N, Chen H, Liu T, Li A (2016) Mechanism of the December 2015 catastrophic landslide at the Shenzhen landfill and controlling geotechnical risks of urbanization. Engineering 2:230–249CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Heng Liang
    • 1
    • 2
  • Siming He
    • 1
    • 3
    • 4
    Email author
  • Xiaoqin Lei
    • 1
    • 3
  • Yuzhang Bi
    • 5
  • Wei Liu
    • 1
    • 2
  • Chaojun Ouyang
    • 1
    • 3
  1. 1.Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengduChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Key Laboratory of Mountain Hazards and Surface ProcessChinese Academy of SciencesChengduChina
  4. 4.CAS Center for Excellence in Tibetan Plateau Earth SciencesChengduChina
  5. 5.Southeast UniversityNanjingChina

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