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r.rotstab: a GRASS-based deterministic model for deep-seated landslide susceptibility over large areas Martin Mergili 1 , Ivan Marchesini 2 , Mauro Rossi 2 , Fausto Guzzetti 2 , Wolfgang Fellin 3 1 Institute of Applied Geology, BOKU University,


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Presentation OGRS 2012 Yverdon, October 2012 r.rotstab: a GRASS-based deterministic model for landslide susceptibility analysis

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r.rotstab: a GRASS-based deterministic model for deep-seated landslide susceptibility over large areas

Martin Mergili1, Ivan Marchesini2, Mauro Rossi2, Fausto Guzzetti2, Wolfgang Fellin3

1 Institute of Applied Geology, BOKU University, Vienna, Austria 2 CNR IRPI, Perugia Italy 3 Geotechnical and Tunnel Engineering, University of Innsbruck, Austria

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> 1 Slope stability modelling > 2 The 3D slope stability model r.rotstab > 3 The Collazzone test area > 4 Model results > 5 Conclusions and outlook

Content

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1 Slope stability modelling

Infinite slope stability model

FSinf > 1: slope is stable FSinf < 1: slope is not stable

> The infinite slope stability model is often coupled with slope hydraulics (SHALSTAB, SINMAP or TRIGRS)

FS inf = R T = c/cos β+N tan ϕ T

γw θs d normal force N =G'cos β c shear force T =G'sin β+S shear resistance R=N tan ϕ+c/cos β β

buoyancy

weight of moist soil

G'=γd d+θs γw d sub−γw d sub ....=γd d+(θs−1)γwd sub

seepage force S=γw d subsin β φ γd

γd ... specific weight of dry soil (N/m³) γw ... specific weight of water (N/m³) c ... cohesion soil + roots (N/m²) φ ... angle of internal friction (°) θs ... sat. water content (vol.-%)

dsub

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Presentation OGRS 2012 Yverdon, October 2012 r.rotstab: a GRASS-based deterministic model for landslide susceptibility analysis

Shallow and deep-seated landslides

4 Photo: USGS

shallow translational failure deep-seated rotational failure The infinite slope stability model is only applicable to shallow translational slides in cohesionless regolith. It shall not be used for modelling of rotational slope failures and deep-seated mass movements

1 Slope stability modelling

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Presentation OGRS 2012 Yverdon, October 2012 r.rotstab: a GRASS-based deterministic model for landslide susceptibility analysis

forces are shown for every second column only

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Slip circle model

slip circle inter-column forces

> The basic static principle is the same as with the infinite slope stability model, but different methods for the application with curved failure planes exist

Bishop (1954), Janbu et al. (1956), Hovland (1977)

1 Slope stability modelling

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3 2 1

Random search for critical slip surface

1 FS = 1.83 2 FS = 1.16 3 FS = 0.67 application with GIS non-trivial no pixel-based approach 1 FS = 0.67

1 Slope stability modelling

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3D slip circle models

> The proof of slide stability is a standard task of geotechnical investigations, many software packages are therefore available > Many programs are based on longitudinal profiles, 3D models exist as well

www.clara-w.com (Hungr, 1988)

> Few attempts with GIS

Xie et al. (2003, 2004a, b, 2006), Marchesini et al. (2009)

1 Slope stability modelling

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GRASS GIS

> GIS-supported 3D slope stability model for >> single landslides with a priori known failure plane >> identification of the most critical slip surface of single slopes >> slope stability mapping for larger areas where the infinite slope stability model is not suitable > Freely available to experts dealing with slope stability problems

2 The 3D slope stability model r.rotstab

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r.rotstab – implementation

data input data preprocessing post-processing of results display and export of results start of simulation shell script r.rotstab.sh with terminal interface run within GRASS GIS simulation C code r.rotstab GRASS raster module

2 The 3D slope stability model r.rotstab

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truncated ellipsoid ellipsoid sphere 2 The 3D slope stability model r.rotstab

Slip surface geometry

possible slip surface geometries

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2 The 3D slope stability model r.rotstab

Orientation of potential slip surfaces

a c x' z' = z z'' a b x y x' y'

d i r e c t i

  • n
  • f

s t e e p e s t s l

  • p

e

(a) ground plot (b) longi- tudinal section along x' α β zb L D r·a r·a r·a r·a P1 P2 P1‘ P2‘ x''

  • ption to skip randomization and to work with defined

parameters randomization of center coordinates, a, b, c, zb and r

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2 The 3D slope stability model r.rotstab

3D safety factor

forces are shown for every second column only

inter-column forces are neglected (Fellenius appoach) sum of stabilizing and destabilizing forces over all columns

modified after Hovland, 1977, Xie et al., 2003, 2004a, b, 2006

FS 3D=∑C (cA+(G 'cos βm+N s) tan ϕ)cos βm

∑C (G 'sin β m+T s)cos β m

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2 The 3D slope stability model r.rotstab

Spatial discretization

bedrock

minimum FS and depth of slip surface if FS < 1 are determined for each raster cell test of many randomly selected slip surfaces

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2 The 3D slope stability model r.rotstab

r.rotstab – logical framework

random ellipsoid critical slip surface? R T G‘, S γd, c, φ, θs, geometry ellipsoid parameters

  • ther ellipsoids

slip surface

  • ther slip surfaces

depth of slip surface factor of safety FS3D

column 1

factor of safety raster cell level input ellipsoid level slip surface level column level R T G‘, S

column 2

  • ther

columns depth of slip surface factor of safety minimum factor of safety FS3D deepest slip surface with FS3D < 1

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2 The 3D slope stability model r.rotstab

Test against established models

Hungr et al., 1989 Lam and Fredlund, 1993 Xie et al., 2006 Leshchinsky et al., 1985 Hungr et al., 1989 Xie et al, 2006 Xie et al, 2006 and others

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2 The 3D slope stability model r.rotstab

Test against established models

cell size / model (a) (b) with C1 (b) with C2 cell size (c) slip surface 1 with / without weak layer (c) slip surface 2 dry (c) slip surface moist Results with r.rotstab (version with Hovland 3D model) 0.02 m 1.43 1.19 1.28 0.5 m 2.03 / 2.02 1.58 1.53 0.05 m 1.38 1.18 1.27 1.0 m 2.10 / 2.05 1.58 1.53 0.10 m 1.35 1.16 1.26 2.0 m 2.04 / 2.02 1.53 1.48 Results with 3DSlopeGIS (Xie et al., 2006) Hovland 3D 1.43 1.19 1.25 2.09 1.57 1.49 Bishop 3D 1.43 1.20 1.31 2.28 1.71 1.62 Janbu 3D 1.40 1.22 1.29 2.04 1.61 1.54 Revised Hovland 3D 1.38 1.18 1.27 2.30 1.67 1.57

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3 The Collazzone test area

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Landslide inventory and lithology

3 The Collazzone test area

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Slope

3 The Collazzone test area

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Geotechnical data

3 The Collazzone test area

Class γd kN m-3 c (d = 1.3 m) kN m-2 c (d = 20 m) kN m-2 φ° θs vol.-% c90 c95 c98 c90 c95 c98 Sand 19 0.5* 0.5* 0.5* 4.0* 4.0* 4.0* 38 40 Clay 22 0.0 1.8 4.5 0.0 15.5 40.0 18 45 Flysch deposit 18 1.8 3.1 4.8 16.0 27.5 44.0 15 45 Gravel, sand, silt and clay 19 1.5* 1.5* 1.5* 13.0* 13.0* 13.0* 30 45 Sand, silt and clay 18 0.9 2.5 4.6 7.5 21.5 42.0 15 45 Sandstone 22 0.0 2.1 4.0 0.0 16.0 34.0 35 45 Conglomerate 22 1.0* 1.0* 2.1 8.5* 8.5* 17.0 35 45 Limestone 22 1.5* 1.5* 1.5* 13.0* 13.0* 13.0* 35 45 Travertine 22 1.0* 1.0* 1.0* 8.5* 8.5* 8.5* 35 45 saturated water content Angle of internal friction cohesion dry specific weight

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4 Model results

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4 Model results

Safety factor for shallow slope stability

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Safety factor for shallow slope stability

4 Model results

True positive (TP): cell predicted as unstable (FS<1) and that contains a landslide, True negative (TN): cell predicted as stable (FS>=1) that does not contain a landslide, False positive (FP): cell predicted as unstable (FS<1) that does not contain a landslide, False negative (FN): cell predicted as stable (FS>=1) that contains a landslide.

TP TN FP FN Four-fold-plots TP TN FP FN TP TN FP FN

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Safety factor for shallow slope stability

4 Model results

True positive (TP): cell predicted as unstable (FS<1) and that contains a landslide, True negative (TN): cell predicted as stable (FS>=1) that does not contain a landslide, False positive (FP): cell predicted as unstable (FS<1) that does not contain a landslide, False negative (FN): cell predicted as stable (FS>=1) that contains a landslide.

TP TN FP FN Four-fold-plots TP TN FP FN TP TN FP FN

The greater the cohesion the higher is the percentage of pixel correctly predicted (TN+TP)

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Safety factor for shallow slope stability

4 Model results

True positive (TP): cell predicted as unstable (FS<1) and that contains a landslide, True negative (TN): cell predicted as stable (FS>=1) that does not contain a landslide, False positive (FP): cell predicted as unstable (FS<1) that does not contain a landslide, False negative (FN): cell predicted as stable (FS>=1) that contains a landslide.

TP TN FP FN Four-fold-plots TP TN FP FN TP TN FP FN

For the lower cohesion, more than the half of stable cells and more than the half of unstable cells are correctly predicted

> <

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Safety factor for deep-seated slope stability

4 Model results

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Safety factor for deep-seated slope stability

4 Model results For the lower cohesion, more than the half of stable cells and more than the half of unstable cells are correctly predicted

> < TP

TN FP FN

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>The model r.rotstab produces preliminary satisfactory results for shallow and deep-seated landslides >Better knowledge of geotechnical parameter as well as of geologic conditions (bedding attitude) is required to further improve the results >Software now has the capacity to test several million slip surfaces per day, further improvement of performance shall be achieved by cloud computing The software is available here: http://www.mergili.at/gis.html under the GPL licence conditions

5 Conclusions and outlook

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Thank you for the attention Martin Mergili martin.mergili@boku.ac.at Ivan Marchesini Ivan.marchesini@irpi.cnr.it

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Landslide susceptibility indices

4 Model results

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Landslide susceptibility indices

4 Model results

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References and further reading

Bishop, A.W., 1954. The use of the slip circle in the stability analysis of slopes. Geotechnique 5(1), 7–17. Griffiths, D.W., Huang, J., de Wolfe, G.F., 2011. Numerical and analytical observations on long and infinite slopes. International Journal for Numerical and Analytical Methods in Geomechanics 35, 569–585. Hovland, H.J., 1977. Three-dimensional slope stability analysis method. Journal of the Geotechnical Engineering Division. Proceedings of the American Society

  • f Civil Engineers 103(GT9), 971–986.

Hungr, O., 1988. CLARA: slope stability analysis in two or three dimensions. O. Hungr Geotechnical Research, Vancouver, BC. Hungr, O., Salgado, F.M., Byrne, P.M., 1989. Evaluation of a three-dimensional method of slope stability analysis. Canadian Geotechnical Journal 26, 679–686. Janbu, N., Bjerrum, L., Kjaernsli, B., 1956. Soil mechanics applied to some engineering problems. Publication 16, Norwegian Geotechnical Institute, Oslo. Lam, L., Fredlund, D.G., 1993. A general limit equilibrium model for 3-D slope stability analysis. Canadian Geotechnical Journal 30, 905–919. Leshchinsky, D., Baker, R., Silver, M.L., 1985. Three-dimensional analysis of slope stability. International Journal of Numerical and Analytical Methods in Geomechanics 9, 199–223. Marchesini, I., Cencetti, C., De Rosa, P., 2009. A preliminary method for the evaluation of the landslides volume at a regional scale. Geoinformatica 13, 277–289. Milledge, D., Griffiths, V., Warburton, J., Lane, S., 2011. Can we use the infinite slope model within catchment scale landslide models given its landslide length assumption? Geophysical Research Abstracts 13, EGU2011–3127. Xie, M., Esaki, T., Zhou, G., Mitani, Y., 2003. Three-dimensional stability evaluation of landslides and a sliding process simulation using a new geographic information systems component. Environmental Geology 43, 503–512. Xie, M., Esaki, Cai, M., 2004a. A GIS-based method for locating the critical 3D slip surface in a slope. Computers and Geotechnics 31, 267–277. Xie, M., Esaki, T., Zhou, G., 2004b. GIS-based Probabilistic Mapping of Landslide Hazard Using a Three-Dimensional Deterministic Model. Natural Hazards 33, 265–282. Xie, M., Esaki, T., Qiu, C., Wang, C., 2006. Geographical information system-based computational implementation and application of spatial three-dimensional slope stability analysis. Computers and Geotechnics 33, 260–274.

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2 The 3D slope stability model r.rotstab

3D safety factor

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Landslide susceptibility indices

4 Model results

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Landslide susceptibility indices

4 Model results

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Landslide susceptibility indices

4 Model results

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2 The 3D slope stability model r.rotstab

Orientation of potential slip surfaces

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2 The 3D slope stability model r.rotstab

3D safety factor

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Optimization of slip surface density

3 The Collazzone test area

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Optimization of slip surface density

3 The Collazzone test area