9oct 4 crosta-monitoring and modelling

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9oct 4 crosta-monitoring and modelling

  1. 1. International Conference, Vajont 1963-2013 October 8-10, 2013 Padua Monitoring and modeling of rock slides and rock avalanches Giovanni B. Crosta (1), S. Imposimato (2), D. Roddeman(2), C. Di Prisco(3), R. Castellanza (1), G. Frigerio(1), S. Utili(4), T. Zhao(4), P. Frattini (1), M. De Caro(1), G. Volpi(1), A. Villa(1) (1)Univ. Studi di Milano-Bicocca, Dept. Earth and Environmental Sciences, Italy (2) FEAT, The Netherlands (3) Politecnico di Milano, Italy (4) Univ of Warwick, UK (5) Univ. of Oxford, UK Crosta G.B.
  2. 2. Topics A short unifying personal journey from monitoring to evolution/runout modeling La Saxe rockslide • ‘rock slides’ can evolve to collapse • assessment of evolution in time and space Effect of erosion Failure Cumulat. Displ. Formation of a continuous failure surface Pre-failure Displacement rate, released en., cumulative displacement 1) Forecasting displacement evolution  model implementable in EWS Reactivations Post failure Critical phase Seasonal, annual activity Collapse Failure Alert PreResidual fail alert time Effect of slope geometry and erodible materials Impact with water t3 t1 t2 2) Spreading and interaction with material along the path: lab scale experiments, field observations, 2D and 3D numerical modeling with a special eye on Vajont rockslide Crosta G.B.
  3. 3. Monitoring displacements Ground surface & deep displacements Understand evolution/state of activity La Saxe rockslide, Italy Sensitivity to perturbations • Seasonal • Sudden acceleration • behaviour in absence of perturbations Thresholds for EWS Multi parametric DMS probe Crosta G.B.
  4. 4. Monitoring displacements Time lag ? ? piezo ? • Multi parametric DMS probe 2011-2013 Failure surface and piezometric head geometry by multiple measuring points  e.g. slide thickness • State of activity at depth  surficial • Delay in activation  Time lag with • Thickness of shear zone  spatial • • Weakening – strengthening Slow continuous and/or stick slip vs deep displacements respect to piezometric level distribution (and inclination) Crosta G.B.
  5. 5. Failure surface geometry Vajont Difficulty at defining and interpreting structurally controlled geometries More relevant when very close to failure/collapse Crosta G.B.
  6. 6. Displacement history Vajont Optical targets Piezometric measurements Well known records Relatively long with respect to collapse Lake level effects Crosta G.B.
  7. 7. 3D FEM: ave material properties Failure  progressive weakening Ave. Material properties leading to a generalized instability Midas - GDS 3D effect of lake impounding on slope stability and activity Thin shear zone along the 3D reconstruction of the failure zone  Bistacchi et al. 300 200 cohesion [kPa] Progressive Shear strength reduction till loss of convergence 250 150 100 50 0 26 24 22 20 18 friction angle [deg] 16 14 12 Crosta G.B.
  8. 8. 3D FEM: ave material properties Displacement # 1/1: Lake level 700 m a.s.l. (c=300 kPa, =26°) Plastic strain rock mass c=300 kPa =26° Crosta G.B.
  9. 9. 3D FEM: ave material properties Displacement # 1/2: Lake level 700 m a.s.l. (c=200 kPa, =22°) Plastic strain Crosta G.B.
  10. 10. 3D FEM: ave material properties Displacement # 1/3: Lake level 700 m a.s.l. (c=100 kPa, =18°) Plastic strain Crosta G.B.
  11. 11. 3D FEM: ave material properties Displacement # 1/4: Lake level 700 m a.s.l. (c=50 kPa, =14°) Plastic strain Crosta G.B.
  12. 12. 3D FEM: ave material properties Displacement # 1/5: Lake level 700 m a.s.l. (c=25 kPa, =13°) Plastic strain Loss of convergence Crosta G.B.
  13. 13. Pre-Collapse velocity Vajont Observed pre-collapse velocity from historical data (62 events) Forecasting long and short term displacement Vajont Up to collapse Problem last measurement before collapse are rare Crosta G.B.
  14. 14. Displacement forecasting: a 1D Visco plastic model • Average constant thickness in large sectors • Prevalent translational displacement • Sliding surface at fixed position, localized shear band with constant thickness H hw (t) Ds • Large displacements, close to or at residual/critical state • Considering inertial dynamic and viscous effects: a pseudo-dynamic Newmark-type approach coupled to a visco-plastic model (Perzyna’s type): delayed-plastic constitutive approach (standard plastic flow rule is modified and the consistency condition removed; di Prisco et al., 2003; Zambelli et al., 2004) Time evolution of visco plastic strain constitutive parameter Viscous nucleus plastic potential effective stress • Includes: weight, seepage force, hydrostatic force, active/passive force • Main Forcing: piezometric level oscillations, cyclic dynamic perturbations Crosta G.B.
  15. 15. Displacement forecasting: 1D Visco plastic model Included in the modeling Viscous nucleus Plasticity function 1. Linear-bilinear kernel 2. Exponential kernel 3. Softening/Flash weakening/rate & statevariable constitutive laws 4. Seepage during lake drawdown 3. Softening/Rate & State-variable law/Flash weakening Thermal diffusivity typical dimension asperity (P) Weakened  Static  Critical velocity Instant. velocity Dietrich –Ruina law: (Dietrich, 1994) Weakening temperature heat cap. per univ vol background T Shear strength of asperity rate state Rice (2006), Beeler, Tullis, and Goldsby. (2008) Kuwano and Hatano. (2011) Ferri, Di Toro, Hirose, Han, Noda, Shimamoto, Quaresimin, and de Rossi (2011)  Vajont Helmstetter et al., 2004; Veveakis et al., 2007; Vardoulakis, 2002; Alonso and Pinyol, 2010 Crosta G.B.
  16. 16. Displacement forecasting: Vajont   calibration on initial displacements Flash weakening/softening (weak = 0.14, Vweak=0.3 – 0.4 mm/hr) Helmstetter et al., 2004 Veveakis et al., 2007 Crosta G.B.
  17. 17. Displacement forecasting: Vajont Flash weakening/softening Helmstetter et al., 2004 Veveakis et al., 2007 Crosta G.B.
  18. 18. 1D visco plastic model: weakly interacting blocks Monitored data Considering interaction forces in terms of lateral frictional resistence or dragging and of front- and back-thrust Model calibration: 2009-2010 Model prediction: 2011-2013 Crosta et al., in press Crosta G.B.
  19. 19. Acceleration  collapse  spreading A short unifying journey from monitoring to evolution/runout modeling Effect of erosion • ‘rock slides’ can evolve to a final collapse • assessment of evolution in time and space 1) Forecasting displacement evolution by a model Effect of slope geometry and erodible materials Impact with water t3 t1 t2 2) Runout observations of spreading and interaction with material along the path: lab scale experiments, field observations, 2D and 3D numerical modeling Large displ.  weakening  geometrical instability  loss of rock mass strength  collapse  erosion  impact reservoir Crosta G.B.
  20. 20. 2D FEM stability & runout Evolution of Tochnog FEM code (Roddeman, 2001, 2013) arbitrary Eul.-Lagr. AEL calculations Isoparametric FE, Euler backward timestepping for numerical stability in time Transport of state variables in space. Stabilised by Streamline Upwind Petrov Galerkin Automatic timestep size and # of iterations, based on unbalanced force error Large deformation material description Updated Lagrange: incrementally objective Lagrangian model, polar decomposition of incremental deformation tensor Incrementally objective to account for large rotations Determination of initial equilibrium stress state with quasi static time-stepping; Inertia included Material laws: Classical elasto-plasticity. M-C, Drucker-Prager yield surface, etc. Non-associated for granular materials rock = 23° surf = 8.7° Shear through no lake Calibrated against depth and time Crosta et al., 2002; Nato Workshop Celano Crosta G.B.
  21. 21. 3D FEM stability & runout 4 sec 16 sec Crosta et al., 2005 EGU; Crosta et al., 2007; EC LessLoss Project 12 sec 8 sec 36 sec 20 sec 50 sec Fully 3D 105.000 hexahedral 8 node elements  = 12-5.7° no lake Calibrated against depth and time Crosta G.B.
  22. 22. Validation: erosion and deposition A=Hi/Li = 3.2 Numerical Experimental Experimental Numerical Numerical Deposition: Interface aggradation Model validation against well controlled lab granulat step collapse tests Crosta et al., 2007; Benchmarking test Hong Kong; Crosta et al., 2007; EGU Wien Crosta et al., 2008 JGR Crosta G.B.
  23. 23. 3D erosion – entrainment - ploughing Frictional and Cohesive material Erodible thickness: 100 m Fold-like Cohesive material Erodible thickness: 25 m Crosta et al., 2008; 2D saturatd soil, Engeo Crosta et al., 2011;3D, WLF2 Rome • radial pattern of deformation • thickness of layer inversely related to runout • deposit area inversely related to thickness of erodible layer • deformation larger in thicker and frictional materials Wedge Thrust-like Crosta G.B.
  24. 24. Thick slide-shallow water interaction Materials Shear stress 2s Slide Froude number Fr = v/(gh)1/2 = 0.26 -0.75 Velocity 10 s 20 s Crosta et al., 2003; EGU Wien Crosta et al., 2011; WLF2 Rome 71.800 triangular elements, ave. size = 4 m, 15.500: landslide, 1.000: old landslide material, 1800: water reservoir ; incompressible, fully inviscid Landslide properties: Mohr-Coulomb material:  = 24 kN/m3;  = 0.23,  = 17°, c = 300 kPa basal plane:  = 7.5°, c = 10 kPa, (Skempton, 1966; Hendron and Patton, 1985; Tika and Hutchinson, 1999) Crosta G.B.
  25. 25. Validation: Materials Velocity field Quasi-rigid slide-deep water interaction Materials Velocity field 0.6 s 0.1 s Velocity field Materials 1.2s 3m 0.7 s 0.2 s 0.8 s 0.3 s 1.4 s 1.6 s 0.9 s 0.5 s 1.8 s 1.0 s 0.4 s 2.0 s Model Validation: 2D modelling of Aknes rockslide Sælevik’s et al. (2009) water tank experiment for an impact velocity of 3.38 m s-1 of a 1 m long “deformable” granular slide, Fr = 1.4 showing a backward collapsing impact crater Crosta et al., 2011; WLF2 Rome Crosta G.B.
  26. 26. Validation: Deformable slide-deep water interaction Model Validation t = 0.24 s (v_max = 4.52m/s) COMPUTED VELOCITY FIELD t = 0.84 s (v_max = 2.29m/s) t = 1.40 s (v_max = 2.22m/s) t = 0.44 s (v_max = 1.57m/s) t = 1.54 s (v_max = 1.62m/s) t = 0.98 s (v_max = 2.29m/s) time time t = 0.56 s (v_max = 1.49m/s) t = 1.12 s (v_max = 2.00m/s) t = 1.84 s (v_max = 1.17m/s) solitary wave t = 0.70 s (v_max = 2.25m/s) t = 1.26 s (v_max = 1.33m/s) Fritz, 2002, Heller, 2007 Outward collapse of the impact crater is observed t = 2.04 s (v_max = 1.07m/s) together with wash back, flow divergence, and propagation of the primary solitary wave Crosta G.B.
  27. 27. 3D: thick slide-shallow water interaction Velocity vectors at 3 s intervals ca 800.000 hexahedrons: ca. 22 x22 m x 18 m Landslide properties: Mohr-Coulomb material: = 24 kN/m3; Ed = 1*1010 Pa;  = 0.23,  = 23°, c = 1 Pa; Basal plane:  = 5.7°, c = 0 kPa (Skempton, 1966; Hendron and Patton, 1985; Tika and Hutchinson, 1999) Crosta G.B.
  28. 28. 3D: thick slide-shallow water interaction Crosta G.B.
  29. 29. 3D: thick slide-shallow water interaction Crosta G.B.
  30. 30. Validation: deposit and water wave limits slide and water max velocity and wave height 100 1000 Max observed water runup (ca. 900 m a.s.l.) VELOCITY (M S-1) 900 80 800 700 60 600 0 sec 500 40 400 300 20 200 water slide Dam Deposit 100 max water height [m] 0 Rockslide limit 0 0 10 20 30 40 50 Max water runup line TIME (S) Scar limit After 51 sec Dam Comparison of the initial and final rockslide boundaries, and reservoir geometry with the computed geometries Deposit Rockslide mass Computed water geometry Scar limit Crosta G.B.
  31. 31. Validation: point trajectories Computed vs “observed”final displacement Pre- and post-failure positions of geological marker points (Rossi and Semenza) Crosta G.B.
  32. 32. Challenging subjects: Coupled DEM-CFD model basal friction angle: weak layer=12° Progressive bond breakage Scaled grain size Hydraulic conductivity Water wave generation Crosta G.B.
  33. 33. Challenging subjects: Collapse over erodible bed Gravel/sand Sand/sand Sand/rigid bed V = 5100 cm3 ; α = 40° V = 5100 cm3 ; α = 55° V = 5100 cm3 ; α = 55° Crosta G.B.
  34. 34. Challenging subjects: space-time evolution, shock wave To describe and model dynamically changing geometry (flow to final deposition)  geometry and boundary conditions  Erodible / non erodible layers #3 – 45° Shock wave propagation #4 - 50° 1 Deposit 2 3 Eroded & redeposited #5 – 60° 2 Upstream growth 3 Initial wave 0.3 m 1 At rest Time 1.0 s Crosta G.B.
  35. 35. Conclusions - Displacement forecasting: • acceleration to collapse • softening/weakening • Easily implemented in EWS - different failure and entrainment modes are involved and replicated • • • formation of thrust-like and fold-like features Debris pushing and sinking basal dragging and wave-like features - Constitutive laws; metastable materials - 3D ‘Fully’ integrated/interacting slide – water systems - Future challenges Crosta G.B.
  36. 36. THANK YOU FOR YOUR ATTENTION Crosta G.B.
  37. 37. Thick slide-shallow water interaction Landslide Volume: ca 275-300 Mm3, runout = 360 m, runup = 140 m estimated ave. velocity = 20-30 m s-1 Reservoir: Volume ca 115 Mm3, mean depth: 100 m; Water runup = 235 m Failure: ca 40-50 s (Ciabatti, 1964), seismic shocks = 97 sec including signal generated by the water wave Slide Froude number Fr = v/(gh)1/2 = 0.26 -0.75 v = slide velocity, h = reservoir water depth Crosta G.B.

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