1. Uncertainty in Earthquake-Induced Deformation Model Estimates: Case History of Calabasas Landslide
Joseph Wartman and Patrick Strenk
Department of Civil, Architectural and Environmental Engineering
Drexel University, Philadelphia, USA

2. Sliding (Rigid) Block Procedure [Newmark 1965]
Sliding Block/Plane
Assumptions
well-defined slip surface
soil is rigid, perfectly plastic
soil does not lose strength during shaking
Failure Mass/Slip Surface

3. Decoupled Procedure 1. Dynamic Response Analyses
2. Sliding Block Analyses
Makdisi and Seed (1978)

4. Fully Coupled Analysis

5. A variety of deformation models, and some basic questions
Which models are most accurate?
Which models are most precise?
How confident can one be with model predictions?
What are the sources of uncertainty in a slope deformation
analysis, and of these, which are the most important?
When is a model become sufficiently detailed for the task?

6. Research Methods Physical models
+ Significant insight to fundamental mechanisms
+ Limited variability in input parameters
- Boundaries can be a concern for dynamic response
- How well do they represent reality?
Numerical simulations
+ Can provide insight to system-level performance
+ Parametric studies
Case Histories
+ Full scale and entirely realistic
How to account for variability/uncertainty in parameters?

7. Research Approach
1) Test a suite of slope deformation models against high quality case histories
2) Capture variation/uncertainty in input parameters (friction angle, water
level, ground motion, etc.) using Monte Carlo simulations
Assumptions
Field measurements correct
Representative samples
Shear surface at its residual strength
Plane strain (2D) conditions
Wavelet-based ground motion generation procedure
No uncertainty associated with location of shear surface and modulus reduction and damping curves

8. Case History Selection
Selected Case Histories
Calabasas Landslide (1994 Northridge)
Ditullio Landslide (1989 Loma Prieta)
Upper Laurel Landslide (1989 Loma Prieta)
La Villita Dam (1985 Michoacan)
Chiquita Canyon C Landfill (1994 Northridge)
Chiquita Canyon D Landfill (1994 Northridge)
Dynamic Soil Properties
Earthquake Ground Motions
Deformation Measurements
Cross-Section
Field-Testing
Laboratory Testing
˜ - Present or “High-Quality” ž - Present or “Fair-Quality” ™ - Not Present or “Poor-Quality”

9. Practice-Oriented Slope Deformation Models (Simple to Complex)
Rigid-Block
Simplified Decoupled
Double-integration (Y-SLIP_PM)
Makdisi & Seed (1978)
Bray et al. (1998)
Simplified Rigid-Block
Newmark (1965)
Sarma (1975)
Franklin & Chang (1977)
Hynes-Griffin & Franklin (1984)
Ambraseys & Menu (1988)
Yegian et al. (1991)
Ambraseys & Srbulov (1994)
Bray & Travasarou (2007)
Jibson (2007) [3 equations]
Saygili & Rathje (2008) [2 equations]
Decoupled
1D FLAC & Y-SLIP_PM
SHAKE & Y-SLIP_PM
2D FLAC & Y-SLIP_PM
Coupled*

10. Simplified Rigid-Block Methods
Slope Deformation Models: Input Parameters
Simplified Rigid-Block Methods
Input Parameters
PGA
PGV Tp
Neq Mw Ms r Ia
Newmark (1965) x -
Sarma (1975)
Franklin & Chang (1977)
Hynes-Griffin & Franklin (1984)
Ambraseys & Menu (1988)
Yegian et al. (1991)
Ambraseys & Srbulov (1994)
Bray & Travasarou (2007)
Jibson (2007) [Method A]
Jibson (2007) [Method B]
Jibson (2007) [Method C]
Saygili & Rathje (2008) [Method A]
Saygili & Rathje (2008) [Method B]

11. Slope Deformation Models: Input Parameters
Methods
Input Parameters
PGA To
D5-95 Tm
Crest PGA
Rock Outcrop
HEA
Rigid block
(Y-SLIP_PM) - x
Makdisi & Seed (1978)
Bray et al. (1998)
1D Decoupled
(FLAC, Y-SLIP_PM)
(SHAKE, Y-SLIP_PM)
2D Decoupled

12. Case History: Calabasas, CA Landslide (Northridge Earthquake)
Pradel et al. 2005
Many test borings
Laboratory test results
Shear wave profiles
Close-vicinity ground motion stations
Subsurface, direct measurements of ground deformation = 5 cm

13. Calabasas: Cross Section

14. Estimation of residual friction angle

15. Estimation of residual friction angle
Liquid Limit, COV = 19%, Source: Phoon & Kulhawy (1999), Distribution: normal

16. Estimation of residual friction angle

17. Groundwater level
GW Table, COV = n/a, Mean +/- 1 m, distribution: triangular

18. Monte Carlo Simulation: Yield Acceleration

19. Monte Carlo Simulation: Yield Acceleration

20. Monte Carlo Simulation: Shear Wave Velocity Profile
Shear Wave Velocity over 30 m (vs30), COV = 3%, (measured), Moss (2008)
Distribution: log-normal

21. Monte Carlo Simulation: Shear Wave Velocity

22. Monte Carlo Simulation: Earthquake Ground Motion

23. Monte Carlo Simulation: Earthquake Ground Motion

24. Monte Carlo Simulation: Earthquake Ground Motion
Wavelet-based generation of spectrum-compatible time-histories,
Mukherjee and Gupta (2002)

25. Monte Carlo Simulation:
Earthquake Ground Motion

26. Monte Carlo Simulation:
Earthquake Ground Motion

27. SHAKE vs. FLAC,
1D Simulations

28. Monte Carlo Simulations: Latin Hypercube Sampling

29. Results: Calabasas

30. Results: Calabasas extreme values large deformations with decoupled
Close to
measured
site-specific decoupled
more constrained
newer models
capture
site-specific

31. Results: Calabasas – differences between two rigid block methods

32. Findings Which models are most accurate? Average percent errors:
Simplified rigid block: -34%
Rigid block: -66%
Simplified decoupled: 192%
Decoupled: -44%
Which models are most precise?
Simpler models generally have
slightly lower standard deviations
simplified
site specific

33. Findings
How confident can one be with model predictions?

34. Findings
What are the sources of uncertainty in a slope deformation analysis, and of these, which are the most important?
Variation in Ky is significant
Ky is dominated by strength
(ground water is less critical)
Strength assessment should be an
important component of an
investigation program

35. Findings When is a model become sufficiently detailed for the task?
Simple approaches may be sufficient when parameters are uncertain
Complex models are not a very good “value”
Users should consider parameter uncertainties when selecting a model
A trade-off exists between:
- Simple models exclude important details resulting in prediction biases
- Complex models can have significant parameter uncertainties
Model development should consider realistic uncertainties in parameters – Complex models are conceptually sound, but paired with real-world uncertainty, these can lead to a wide range of responses

36. Findings
Under-prediction may result from: distributed straining and/or volumetric compression within soil mass, model limitations (e.g. tuning ratio effects)
Wartman et al. 2003

38. Monte Carlo Simulation: Earthquake Ground Motion