1. SAFER Project - FINAL MEETING Elin Skurtveit & Amir M. Kaynia - NGI
Earthquake-induced Landslides Hazard zonation in Campania – application and implementation in ArcGIS
SAFER Project - FINAL MEETING
Elin Skurtveit & Amir M. Kaynia - NGI

2. Initial objectives
Calibrate a landslide susceptibility model based on topographic and lithological properties of slope and combine it with a deterministic model based on:
mechanical parameters of the soil
geometrical properties of the slope
characteristics of the earthquakes
Validate the model through documented case histories and database of historical landslides.
Establish a GIS-based methodology to couple landslide model to Shake maps in order to produce real-time slide maps.
In cooperation with AMRA and using the shake maps generated in WP2, apply the seismic landslide susceptibility model to a study region in Campania, Italy.
Establish a model for pore pressure generation/dissipation under cyclic loading of earthquake and develop computational tool for post-earthquake landslides.

3. Outline of presentation
Empirical Slope Deformation Analysis
Verification of models for landslide prediction
Implementation of landslide prediction model in ArcGIS
Application of ArcGIS model to test site in Campania, Italy

4. Empirical Slope Deformation Analysis
Factor of Safety
Why Translational Slide Model?
Failures in natural slopes are typically shallow and they have small thickness to length ratios
c = soil cohesion
h = slab thickness
b = slope angle
f = internal friction angle of soil
g ,gw = unit weight of soil and water
n = percentage of saturated slab thickness
Yield Acceleration of Slope
ay = yield acceleration of the slope
(Applied in the direction parallel to slope)

5. Empirical Slope Deformation Analysis
Sliding Displacement Prediction Models
California Method (Blake et. al. 2002)
The predictive equation developed by Bray and Rathje (1998) is presented in Recommended Procedures for Implementation of DGM Special Publication 117: Guidelines for Analyzing and Mitigating Landslide Hazards in California
u = permanent displacement in centimetres
D5-95 = Significant duration parameter (time difference
between 5% and 95% development of Arias Intensity)
kmax = maximum horizontal equivalent loading coefficient
Standard error for this equation is 0.35 in log 10-units
(Limited number of strong motions used were available)
Bray and Rathje (1998)

6. Empirical model of landslide:
1. Establish Factor of safety against failure (H = depth to sliding, C = soil’s shear strength)
2. Calculate yield acceleration of slope:
3. Calculate slope’s displacement (D5-95 = significant duration, kmax = peak ground acceleration in g):

7. Verification of model for landslide prediction
Four Case histories of observed landslide or large slope displacements were used:
Landslide movement during Northridge Earthquake
Debris slump caused by Suusamyr Earthquake
Yokowatashi landslide during Niigata-Ken Chuetsu earthquake
Landslide movement during Coyote Lake Earthquake

8. Verification of model (cont.)
Results of comparisons with case histories of landslides are encouraging and indicate a fairly good performance of the model.
The model is also able to explain the upper bound of historical landslides, established by Keefer (1984), as function of distance and earthquake magnitude.
This model is being implemented in a GIS for mapping of landslide zones

9. Application of ArcGIS Model to test site in Campania, Italy
Study area – Campania
Destra Sele River Basin
Available data (provided by AMRA and INGV)
Digital elevation model
Geology
Geotechnical parameters
Shake Map
Irpinia 1980 earthquake

10. Earthquake data
Peak Ground Acceleration map from the Irpinia earthquake
Provided by INGV

11. Geology of the Study area
Fiorillo & Wilson, 2004

12. Implementation of landslide prediction model in ArcGIS
Raster Calculation and Map Algebra functions.
Example:
The result of [Inlayer1] + [Inlayer2] / 2 results in an output grid displaying the mean value for every cell.
Raster slope map calculated from Digital Elevation Model
Cellsize: 40 m x 40 m

13. Implementation of landslide prediction model in ArcGIS
Input raster layers
Geology & geotechnical parameters
Geometry of slope
ArcGIS Raster calculation model for factor of safety and the yield acceleration of the soil

14. Implementation of landslide prediction model in ArcGIS
Calculate slope’s displacement, u:
Input raster layers
PGA
Soil strength / safety factor
Magnitude and distance
D5-95 = significant duration,
kmax = peak ground acceleration in g
Raster calculation model for soil displacement

15. ArcGIS displacement map
Slope Displacement in cm

16. Sliding Displacement Evaluation – Comparison with the Extended Models
Reference
Saygili and
Rathje (2008)
Bray and Travasarou (2007)
Blake et. al. (2001)*
GM Parameters
PGA,M
PGA,PGV
PGA
PGA, D5-75
Min Displacement
0 cm
Max Displacement
76 cm
78 cm
97cm
171 cm
0 - 5 cm
99.44%
99.41%
99.37% cm
0.31%
0.32%
0.34%
0.26% cm
0.11%
0.10%
0.07%
0.12%
> 30 cm
0.14%
0.13%
0.18%
0.25%
The results from various recent models compare very well with the California Model*
ShakeMaps indicate that PGA and PGV values range from around 0.05 to 0.15 g and 5 to 20 cm/s over most of the slopes, respectively.
It is observed that the zones corresponding to the sliding displacements greater than 5 cm cover a very small portion (around 0.55%) of the study region. The reasons for low hazard potential are:
Slopes have relatively high shear strength
Severity of the ground shaking is not strong

17. Conclusions
Verification of slope displacement model for landslide prediction using case histories.
Application of landslide prediction model to test site in Italy.
Results shows how a GIS-based method can be implemented for near real-time prediction and mapping of landslides.
Results from various recent slope deformation models compare very well with the California Model used in this study.