Strength degradation and progressive failure in massive ...€¦ · 2 29th Canadian Geotechnical Colloquium (Saskatoon, Sept. 20th, 2005) 3of 54 The difficulty - rock masses are complex

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29th Canadian Geotechnical Colloquium (Saskatoon, Sept. 20th, 2005)

2929thth Canadian Geotechnical ColloquiumCanadian Geotechnical Colloquium::

Erik Eberhardt Erik Eberhardt –– Geological Engineering/EOS, University Geological Engineering/EOS, University of British Columbia, Vancouver, Canadaof British Columbia, Vancouver, Canada

Strength degradation and Strength degradation and progressive failure in massive rock progressive failure in massive rock

slopesslopes

(the role of advanced numerical methods (the role of advanced numerical methods and geotechnical field measurements in and geotechnical field measurements in

understanding complex mechanisms)understanding complex mechanisms)

1 of 54

29th Canadian Geotechnical Colloquium (Saskatoon, Sept. 20th, 2005) 2 of 54

Despite improvements in recognition, prediction and mitigative measures, rock slope failures still exact a heavy social, economic and environmental toll in mountainous regions around the world.

Population and economic growth has demanded expansion of habitat, lifelines and natural resources. However, the short history of human development in many regions makes the evaluation of potential landslide hazards and appropriate countermeasures very difficult.

… the 1915 Jane Camp rockslide ranks as B.C.’s worst natural disaster with 56 deaths. Two days prior to the event, the inspected mountainside was deemed “solid”.

… rain triggered landslides in the Alps resulted in 37 deaths and $600 million in damage during Oct. 2000.

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The difficulty - rock masses are complex systems!

Often, field data (e.g. geology, geological structure, rock massproperties, groundwater, etc.) is limited to surface observations and/or limited by inaccessibility, and can never be known completely.

Key Rock Slope Stability Issues to be Discussed:Key Rock Slope Stability Issues to be Discussed:


•• Complexity & UncertaintyComplexity & Uncertainty

•• Phenomenological Phenomenological ––vsvs-- Mechanistic ApproachesMechanistic Approaches–– Temporal PredictionTemporal Prediction–– Spatial PredictionSpatial Prediction

•• Where we are:Where we are:–– Integration of Geotechnical Data Sets Integration of Geotechnical Data Sets

and Advanced Analysesand Advanced Analyses

•• Where we need to go:Where we need to go:–– Progressive Failure in Rock SlopesProgressive Failure in Rock Slopes

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Doug Stead (SFU, Canada)

Acknowledgements:Acknowledgements:

Simon Loew (ETH Zurich, Switzerland)

Peter Kaiser (Mirarco, Canada)

Mark Diederichs (Queens, Canada)

Hansruedi Maurer(ETH Zurich, Switzerland)

Keith Evans(ETH Zurich, Switzerland)

5 of 45


Heike Willenberg (Eng Geol., ETH Zurich)

Acknowledgements Acknowledgements –– Grad Students:Grad Students:

Florian Ladner(Eng. Geol., ETH Zurich)

Björn Heinke(Geophysics, ETH Zurich)

Tom Spillmann (Geophysics, ETH Zurich)

6 of 45

Benoit Valley (Eng Geol., ETH Zurich)

Luca Bonzanigo(Eng. Geol., ETH Zurich)

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Complexity & Uncertainty:Complexity & Uncertainty:

Morgenstern’s Casagrande Lecture (1995):

Parameter Uncertainty: concerned with spatial variations, e.g. rock mass strength, and the lack of data for key parameters.

Model Uncertainty: arises from gaps in the scientific theory that is required to make predictions on the basis of causal inference.

Human Uncertainty: can range from simple human error to corruption.

correct answer

20

10

0within±50%

±15-25%

±15-25%

within±50-100%

MHA prediction competition –collapse height of slope in soft clay. A substantial amount of shear strength data was provided to the 31 participants.


Rock Slope Toolbox:Rock Slope Toolbox:

MappingMapping –– geological, geotechnical, geological, geotechnical, geomorphologicalgeomorphological, hydrogeological, hydrogeological

Hun

gr e

t al

.(20

05)

Willenberg et al. (2004)

5

Schindler et al. (1993)



Empirical Design Empirical Design –– Run out susceptibilityRun out susceptibility

Randa Rockslide(April 18th& May 9th, 1991 events)

• Heim (1932): 1st use, defines travel angle• Scheidegger (1973): empirical prediction• Hsü (1975): links centres of gravity• Evans & Hungr (1993): shadow angle• Ayala et al. (2003): susceptibility mapping



MonitoringMonitoring –– geodetic, extensometers, crack meters, tilt metersgeodetic, extensometers, crack meters, tilt meters

afte

r Te

rzag

hi(1

950)

6

after Terzaghi (1950)

afte

r Fu

kuzo

no(1

990)

• Saito (1965): creep rupture life• Kennedy & Niermeyer (1970): Chuquicamata• Voight (1989): creep velocity & acceleration• Fukuzono (1990): inverse velocity• Salt (1988): empirical alarm levels• Crosta & Agliardi (2003): alert thresholds


Temporal Prediction of Failure:Temporal Prediction of Failure:

GrimselstrasseGrimselstrasse, CH , CH (2000)(2000)

road closedroad closed

water injection water injection down tension crack down tension crack ((~ ~ 9000 l/min)9000 l/min)

blast blast –– 19 19 tonnestonnes of of explosives explosives (for 150,000 (for 150,000 mm33 of rock)of rock)


Temporal Prediction of Failure:Temporal Prediction of Failure:

7

Prediction of Landslide Prediction of Landslide BehaviourBehaviour

road closedroad closed

water injection water injection down tension crack down tension crack ((~ ~ 9000 l/min)9000 l/min)

blast blast –– 19 19 tonnestonnes of of explosives explosives (for 150,000 (for 150,000 mm33 of rock)of rock)


afte

r Gr

uner

(200

3)


8


afte

r Fu

kuzo

no(1

990)

Limiting Factors: • Focuses on surface measurements, ignoring changes in behaviour with depth.

• Technique applied in the same way regardless of failure mode (translational slide, topple, etc.) and/or data source (crack meter, geodetic monuments, etc.).


Rock Slope Toolbox Rock Slope Toolbox -- Empirical Prediction:Empirical Prediction:



Phenomenological approaches - are ‘holistic’ as they disregard details of the underlying mechanisms while concentrating on the overall performance of a system.

Mechanistic approaches - on the other hand, try to break the problem/system down into its constituent parts to understand the cause and effect relationships (and their evolution), which govern the behaviour of the system.

9


Honest look at our toolbox:Honest look at our toolbox:

Mapping

Hoe

k et

al.

(199

5)

Rock Mass Characterization

Instrumentation

Implausible Compute69%69%

Warning

8%8%

Error

23%23%

Implausible Compute69%69%

Warning

8%8%

Error

23%23%

Prediction of Landslide Prediction of Landslide BehaviourBehaviourUnderstanding Landslide Understanding Landslide BehaviourBehaviour

Eberhardt et al. (2002)


Impossible Compute29%29%

Warning

4%4%

Error

58%58%

Crash

8%8%

Impossible Compute29%29%

Warning

4%4%

Error

58%58%

Crash

8%8%

Crill

y(1

993)

Survey of nine commonly used geotechnical modellingprograms and their response to input data.

10

Hybrid MethodsHybrid MethodsRock mass represented as Rock mass represented as an initial continuum or an initial continuum or discontinuum.discontinuum.Procedure allows Procedure allows modellingmodellingof intact rock of intact rock behaviourbehaviour, , discontinuity interactions, discontinuity interactions, and the generation of new and the generation of new fractures through brittle fractures through brittle fracture and adaptive refracture and adaptive re--meshing algorithms.meshing algorithms.

Continuum MethodsContinuum MethodsRock/soil mass behaviour Rock/soil mass behaviour represented as a represented as a continuum.continuum.Procedure exploits Procedure exploits approximations to the approximations to the connectivity of elements, connectivity of elements, and continuity of and continuity of displacements and stresses displacements and stresses between elements.between elements.

Discontinuum MethodsDiscontinuum MethodsRock mass represented as Rock mass represented as a assemblage of distinct a assemblage of distinct interacting blocks or interacting blocks or bodies.bodies.Blocks are subdivided into Blocks are subdivided into a deformable finitea deformable finite--difference mesh which difference mesh which follows linear or nonfollows linear or non--linear linear stressstress--strain laws.strain laws.



Slope Failure InitiationSlope Failure Initiation


afte

r Fu

kuzo

no(1

990)

Temporal prediction:

Almost exclusively empirical.

Constitutive Models:

Rarely include time.


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Spatial Prediction Spatial Prediction –– Forward AnalysisForward Analysis


Static Analysis Linked with Static Analysis Linked with RunoutRunout AnalysisAnalysis


DAN 3DDAN 3D - Ph.D. Thesis: Scott McDougallSupervisor: Prof. Oldrich Hungr

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Integrating Data SetsIntegrating Data Sets

Campo Vallemaggia, CH


Geology - metamorphic gneisses & schistsMechanism – translational slide (30° SSE)Surface Area - ~ 6 km2

Total Volume - ~ 800,000,000 m3

Average Velocity - ~ 5 cm/yearMaximum Depth - ~ 300 m

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Integrating Data Sets Integrating Data Sets –– Campo Campo VallemaggiaVallemaggia


Bonzanigo et al. (2005)




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Campo Campo VallemaggiaVallemaggia –– Block KinematicsBlock Kinematics



1300

1350

1400

1989 1990 1991 1992 1993 1994 1995 1996 1997 1998Bor

ehol

e H

ead

(m)

788

788.2

788.4

788.6

788.8

1989 1990 1991 1992 1993 1994 1995 1996 1997 1998Geo

detic

Mov

emen

t (m

)

0

1

2

3

4

Vel

ocity

(mm

/day

)

drainage adit opened

critical threshold at 1390 m

Bonz

anig

o et

al.

(200

1)



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Discontinuum: HDiscontinuum: H--M Coupled AnalysisM Coupled Analysis


Eber

hard

t et

al.

(200

5)

0.00

0.50

1.00

1.50

20000 60000 100000

Time Steps

Pore

Pre

ssur

e (M

Pa)

adit level

20 m above adit

40 m above adit

60 m above adit


0.00

0.50

1.00

1.50

20000 60000 100000

Time Steps

Pore

Pre

ssur

e (M

Pa)

adit level

20 m above adit

40 m above adit

60 m above adit

drainageadit

opened



16

0.01

0.10

1.00

10.00

20000 60000 100000

Time Steps

X -

Dis

plac

emen

ts (m

)

without pore pressures (i.e. dry slope)

without drainage adit

with drainage

aditdrainage adit

opened


Campo Vallemaggia:Campo Vallemaggia:DistinctDistinct--element models suggest that very element models suggest that very little drainage is required (approximately 10 little drainage is required (approximately 10 l/s) to significantly reduce pore pressures and l/s) to significantly reduce pore pressures and to stabilize the slope.to stabilize the slope.

Deep Drainage:Deep Drainage:Fracture permeability corresponds to low Fracture permeability corresponds to low storativities, therefore large water inflows storativities, therefore large water inflows through drainage are not necessary to achieve through drainage are not necessary to achieve significant reductions in head.significant reductions in head.









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“And as a rock falls precipitate from some mountain-crest, torn thence by the wind, or washed forth by the swollen rains, or loosened by the stealthy lapse of years; under mighty impulse the destroying cliff crashes in abrupt descent and bounds over earth, involving in its train forests and herds and men…”

Virgil (20 BCE), “The Aeneid”

photo by H. Willenberg

Understanding Rock Slope Understanding Rock Slope BehaviourBehaviour


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1991 1991 RandaRanda RockslideRockslide

data

fro

m S

wiss

Met

eo

photo by H. Willenberg

Schi

ndle

r et

al.

(199

3)

current instabilitycurrent instability


Cumulat

ive

Dam

age,

wAE

Normalized Stress (σ/σcd)

cohesion

damage

Relative Cohesion

shear strainshear strain horizontal displacementshorizontal displacements

Cumulat

ive

Dam

age,

ωAE

Normalized Stress (σ/σcd)

cohesion

damage

Relative Cohesion

current instability

Eber

hard

t et

al.

(200

4)


• Bjerrum (1967): progressive failure in clay slopes• Zienkiewicz et al. (1975): strength reduction• Stacey (1981): extension strain• Martin (1997): cohesion loss & stress path• Cooper et al. (1998): Selborne cutting experiment• Leroueil (2001): mechanisms of progressive failure in soil• Hajiabdolmajid & Kaiser (2002): strain sensitivity• Diederichs et al. (2005): damage & heterogeneity

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Progressive Failure in Rock SlopesProgressive Failure in Rock Slopes

Natural stress distributions …


Rock bridges & brittle fracture processes …

Progressive Failure Progressive Failure –– Hybrid FEM/DEMHybrid FEM/DEM

intra-element fracture

inter-element fracture



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Slope Failure Initiation Slope Failure Initiation –– Hybrid FEM/DEMHybrid FEM/DEM

Please Wait..

Project rankine5

ELFEN

Rankine tensile fracture constitutive model

To = 0.175 MPaE = 30 GPaGIC = 200 N/m

May 9th, 1991

April 18th, 1991 intra-element fracture

inter-element fractureEberhardt et al. (2004)


Internal Shearing & Strength DegradationInternal Shearing & Strength Degradation

… distinct-element strain-softening model showing development of Prandtl-type yield zone at base of slide surface and propagation of tensile damage upwards through intact slide mass.

yield

tensile failure

εp c (MPa)

φ (°)

To (MPa)

0 10 5 1 0.001 1 20 0.1 0.002 0.1 40 0 0.005 0.1 40 0

Eber

hard

t et

al.

(200

4)


21

Mohr-Coulomb constitutive modelwith Rankine tensile fracture April 18th

May 9th

April 18th

current instability

current instability

3pe−∆

1pe+∆

3s

1s

Extension Extension StrainStrain


Stead et al. (2005)

The The RandaRanda Rockslide LaboratoryRockslide Laboratory

? ??

?

Existing Fracture

Fracture Initiation

Surface/SubsurfaceDisplacements

Pore Pressures

MicroseismicEmissions


22

RandaRanda Rockslide LaboratoryRockslide Laboratory


Willenberg (2004)Heincke (2005)Spillmann (2005)


Rockslide processes

Geological investigations

Geophysical investigations

3-D geological model


23


Rockslide processes

Geological investigations

Geophysical investigations

3-D geological model

Geotechnical monitoring

Microseismic monitoring

Numerical modelling

Kinematics of the rockslide

topp

ling

slid

ing

rota

ting

?


Known active geological structures

Addition of assumed basal sliding surface and auxiliary fractures

Rock mass properties and constitutive models

Block geometry

discontinuum modelling (UDEC)

Calculated displacement patterns resulting from

unstable situation

Comparison of modelled & measured displacements, including:• Surface displacements• Block displacements/rotations at

depth derived from inclinometer & extensometers

Will

enbe

rg (2

004)


24


Modelled Measured in SB120

topp

ling

slid

ing

topp

ling

slid

ing

rota

ting

Will

enbe

rg (2

004)


RandaRanda Rockslide LaboratoryRockslide LaboratoryW

illen

berg

et

al.(

2004

)

Geometries where the mode of measured displacements could be partially reproduced by numerical modelling:• planar sliding surface

(no agreement with geological model)• step-path sliding surface

(agreement with geological model)


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1.5 2 2.5 3 3.5

A5- V

A5- N

A5- E

B4- V

B4- N

B4- E

A4- V

A4- N

A4- E

Event: 9. April, 2002, 04:19

Time [s]

Spillmann (2005)

The The RandaRanda Rockslide LaboratoryRockslide Laboratory


raw event signalraw event signal

incr

easing

dista

nce

from

sou

rce

incr

easing

dista

nce

from

sou

rce

100100--500 Hz 500 Hz bandpassbandpass filterfilter

incr

easing

dista

nce

from

sou

rce

incr

easing

dista

nce

from

sou

rce

Spillmann (2005)


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LongLong--term Visionterm Vision::To better integrate detailed geotechnical field To better integrate detailed geotechnical field measurements with statemeasurements with state--ofof--thethe--art numerical art numerical modelling to better understand the spatial and modelling to better understand the spatial and temporal evolution of rockslide processes and failure temporal evolution of rockslide processes and failure mechanisms.mechanisms.

LongLong--term Vision:term Vision:In doing so, our goals are to be able to In doing so, our goals are to be able to follow coupled rock mass stability follow coupled rock mass stability problems from their initial stages to problems from their initial stages to catastrophic failure, thus using numerical catastrophic failure, thus using numerical modelling to model the complete failure modelling to model the complete failure process from initiation, through process from initiation, through transportation to deposition.transportation to deposition.


ConclusionsConclusions

As demonstrated through the example of Campo Vallemaggia, field and instrumentation data both provide an important means to constrain numerical models (in this case, to better understand the performance of rock slope mitigation measures).

At the same time, as shown in the case of Randa, it must be recognized that most in situ measurements are affected by the same issues of rock mass complexity and variability as the numerical analyses they are used to constrain, and therefore require a similarly large degree of interpretation.

Geological & Geotechnical Data

Numerical Models

As such, if advances are to be made in the spatial and temporal understanding/prediction of natural slope behaviour, more emphasis needs to be placed on the integration of complex data sets. Iterative approaches should be taken where, for example, rock slope deformation data is used to constrain numerical analyses, but equally so, numerical analyses are used to constrain the interpretation of complex rock slope deformation data.


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Conclusions & Further WorkConclusions & Further Work

Continued advances in computing power requirements are much welcome as problems move into 3-D and increase in complexity (e.g. inclusion of brittle fracturing, hydro-mechanical coupling, etc.). At the same time, much needed improvements are likewise required with respect to engineering geological/geotechnical data collection methodologies.

However, this primarily applies to spatial prediction. To move closer towards better understanding the temporal evolution of massive rock slope failures, sub-surface processes involving rock mass strength degradation and progressive failure must be considered.


Numerical techniques have demonstrated significant potential for furthering our understanding of rock slope deformation and failure mechanisms/processes and the associated risk.

Stea

d et

al.

(200

5)

“Everything should be made as simple as possible…

Einstein


Rock Slope Toolbox: Aiding the Judgment ProcessRock Slope Toolbox: Aiding the Judgment Process

“Numerical modelling should not be used as a substitute for thinking, but as an aid to thought

and engineering judgment”

The more complex the model, the more input parameters it requires and the harder it becomes to determine these parameters without extensive, high quality (and of course, expensive) laboratory testing;

As such, we should always begin by using the simplest model that can represent the key behaviour of the problem, and increase the complexity as required.

but not simpler”.

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Strength degradation and progressive failure in massive ...€¦ · 2 29th Canadian Geotechnical Colloquium (Saskatoon, Sept. 20th, 2005) 3of 54 The difficulty - rock masses are complex