Post on 31-Jan-2021
RAINFALL-INDUCED FAILURES OF NATURAL SLOPES
IN TROPICAL REGIONS by
MUHAMMAD SURADI
This thesis is presented for the degree of
Doctor of Philosophy
of
The University of Western Australia
School of Civil, Environmental and Mining Engineering
March 2015
i
Declaration
“I hereby certify that the work embodied in this Thesis is the result of original
research and has not been submitted for a higher degree to any other University or
Institute”
Muhammad Suradi
October 2014
ii
ABSTRACT
Rainfall-induced slope failures are one of the most damaging natural hazards in the
world, with slope failures occurring every rainy season throughout the world. The
occurrences often cause tremendous losses and show an increasing frequency in the
last decade. These slope failures are likely to occur on natural slopes, which are
heterogeneous due to the processes of natural soil formation, unlike constructed
slopes that are designed and mechanically placed and compacted to withstand
specified loads. Simplifications are commonly applied in slope stability analyses, such
as the use of deterministic analysis methods, to make analyses of stability problems
tractable. Such simplifications ignore the real nature of many controlling factors, such
as rainfall intensity fluctuation and patterns as well as spatial variability of soil
properties, potentially leading to inaccurate results. In this thesis, numerical modelling
was used for coupled seepage and slope stability analyses to consider more realistic
representations of the controlling factors in evaluating rainfall-induced slope failures.
The majority of occurrences are shallow slope failures, so only shallow failure
mechanisms were considered throughout this thesis.
The Jabiru landslide, which occurred during extreme rainfall in February and March
2007 in the region of Arnhem Land, Northern Territory of Australia, was selected as a
case study for this thesis. High resolution (hourly) rainfall data was applied in slope
stability analyses to take account of fluctuating intensities of rainfall. In-situ hydraulic
conductivity obtained from field tests using a tension infiltrometer/disc permeameter
was also considered in these analyses. Parametric studies were carried out to
investigate the effect of variation of the controlling factors, both rainfall events and
soil properties, on rainfall-induced slope failures. Results indicated that the Jabiru
landslide could have been predicted to occur due to the extreme rainfall. This study
also highlighted the role of various aspects of the controlling factors, including
rainfall intensity, duration, resolution and pattern as well as hydraulic and shear
strength properties of soils, in the slope failures.
Analyses were performed to take account of spatial variability of soil properties,
particularly hydraulic conductivity which significantly contributes to the magnitude
and rate of infiltration into a slope. A roadside slope of the Great Eastern Highway at
Sawyer’s Valley site (about 40 km northeast of Perth) was selected to characterise
iii
spatial variability of hydraulic conductivity. Numerous in-situ permeability tests were
conducted, and the spatial variability characterised using statistical techniques. A
parametric study was carried out to investigate the effect of spatial variability on
rainfall-induced slope failures for different slope inclinations and hydraulic
conductivities and other general cases. The results indicated that the spatial variability
generally causes a delay in failures of slopes triggered by rainfall, which was not
expected, based on previously published studies. The result is attributed to the
processes by which residual soils form (e.g. chemical weathering), which produce a
heterogeneous distribution of properties such as density (and hence permeability).
This differs from previous studies where rapid generation of positive pore pressure in
slopes during rainfall were predicted, due to deeper soil layers having lower hydraulic
conductivity (which may be a reasonable assumption for transported soils). This study
also showed that the effect of spatial variability of hydraulic properties on slope
failures is insignificant for different slope inclinations and high-conductivity slopes
but is much more significant for low-conductivity slopes.
To develop robust and inexpensive landslide prediction methods for areas where
numerous steep natural slopes occur, existing methods were evaluated, and where
appropriate, modified. The evaluated landslide prediction methods included the
rainfall intensity-duration-based method, the antecedent rainfall-based method and
rainfall intensity-frequency-duration (IFD) method. The landslide prediction method
based on antecedent rainfall was shown to hold much promise, particularly when
tailored to a particular region using typical soil strength and hydraulic parameters. A
simple screening tool that includes monitoring of daily rainfall events and occasional
measurement of in-situ water content could be invaluable to provide an early warning
system for prediction likelihood of landslides in areas of the world where use of
sophisticated instrumentation is not possible.
iv
ACKNOWLEDGEMENT
First and foremost, I would like to express my deepest gratitude to my supervisor,
Winthrop Professor Andy Fourie, for his guidance, inspiration, generous support,
constant encouragement and consistent feedback throughout my four-year study.
Moreover, his attitude towards research will be an invaluable stimulation for the rest
of my life.
I would also like to thank my co-supervisor, Professor Richard Durham, for his
invaluable feedback on my writing and his assistance of review comments during the
drafting of my PhD thesis.
I specially want to thank my former co-supervisor, Winthrop Professor Martin Fahey,
who invited me to the University of Western Australia and provided assistance for
initial settlement. His friendly style makes me easy to communicate with him.
I appreciate the financial support from the General Directorate of Higher Education
(GDHE) of Indonesia and Ujung Pandang State Polytechnic (UPSP) for me to pursue
a PhD study at the University of Western Australia (UWA).
I would like to thank Dr Mike J. Saynor for his wonderful assistance during fieldwork
at the Jabiru site, Northern Territory of Australia. I would also like to thank Dr Joanne
Edmondston for taking time to proofread initial draft of this thesis. Special thanks also
go to Dr Alsidqi Hasan, Dr Tutun Nugraha and Dr Agus S. Muntohar for their fruitful
discussion and friendship, as well as my relative Dr Andi Shiddiq Yunus for his great
support and companionship during the first half of my study period.
I am indebted to Keith Russell and his team for their IT support and our wonderful
admin staff Charlie Askew. My acknowledgement also goes to Alex Duff, Claire
Bearman, Usha Mani, Behnaz Abdollahzadeh, Masoomeh Lorestani for their support
and friendship during laboratory tests.
I am grateful to my dear friends and colleagues, Jun Li, Yanyan Sha, Amin
Rismanchian, Megan Walske, Dezheng Lao, Yusuke Suzuki, David Reid, Jinglong
Gao, Gonzalo Souza, Stefanus Safinus, Neyamat Ullah, Azrul Muttalib, Fauzan
Sahdi, Bassem Youssef, for their warm friendship.
v
Last but not least, infinite thanks and appreciation to my family: my respected and
beloved father Andi Mappasulle and mother Andi Dinar for their genuine love and
invaluable and perpetual support, my sisters and brothers for their encouragement and
prayers, my dearest wife Andi Nurhayati and children Andi Fazlul Nurhadi, Andi
Sofyan Hadi, Andi Nurdian Musfira and Andi Aisya Haliza for their wonderful love,
support and understanding during all this time.
vi
Table of Contents
Declaration ................................................................................................................................ i
Abstract .................................................................................................................................... ii
Acknowledgement .................................................................................................................. iv
Table of Contents ................................................................................................................... vi
List of Figures .......................................................................................................................... x
List of Tables ......................................................................................................................... xx
List of Symbols ..................................................................................................................... xxi
Chapter 1: INTRODUCTION ............................................................................................... 1
1.1 Research background .................................................................................................... 1
1.2 Occurrences of slope failures triggered by rainfall ....................................................... 5
1.3 Aims and scope of the research .................................................................................... 9
1.4 Thesis outline ............................................................................................................... 10
1.5 Publications .................................................................................................................. 11
Chapter 2: LITERATURE REVIEW .................................................................................. 13
2.1 Introduction ................................................................................................................ 14
2.2 Slope failure mechanisms .......................................................................................... 14
2.2.1 Characterising residual soil slopes in tropical regions .................................... 14
2.2.2 Contribution of controlling factors to slope failure ........................................ 19
2.2.3 Types of slope failure mechanisms ................................................................. 22
2.3 Seepage analysis ......................................................................................................... 23
2.4 Slope stability analysis ............................................................................................... 27
2.4.1 Limit equilibrium method (LEM) ................................................................... 28
2.4.2 Finite element method ..................................................................................... 31
2.4.3 Probabilistic method ....................................................................................... 34
2.5 Characterising the spatial variability of soil properties .............................................. 35
2.5.1 Classical statistical measures of soil properties ............................................. 36
2.5.2 Spatial variability of soil properties ................................................................ 38
2.5.3 Published data of inherent soil variability ....................................................... 41
2.6 Prediction of rainfall-induced slope failures ............................................................... 43
2.6.1 Empirical correlation between rainfall intensity-duration and landslide occurrences ..................................................................................................... 43
vii
2.6.2 Empirical correlation between antecedent-main rainfall and landslide occurrences ..................................................................................................... 44
2.6.3 Approximate method ...................................................................................... 46
2.7 Research hypotheses ................................................................................................... 51
2.8 Summary ..................................................................................................................... 53
Chapter 3: METHODOLOGY OF RESEARCH .............................................................. 54
3.1 Introduction ................................................................................................................. 54
3.2 Study areas .................................................................................................................. 55
3.2.1 Jabiru ............................................................................................................... 55
3.2.2 Sawyer’s Valley .............................................................................................. 58
3.2.3 Boddington ...................................................................................................... 60
3.3 Research procedure ..................................................................................................... 61
3.3.1 Preparation ...................................................................................................... 63
3.3.2 Data collection ................................................................................................ 64
3.3.3 Data analysis and verification ......................................................................... 66
3.3.4 Analysis modelling ......................................................................................... 67
3.4 Field and laboratory investigations ............................................................................ 68
3.4.1 Soil-sampling and field tests ........................................................................... 68
3.4.2 Laboratory tests ............................................................................................... 73
3.5 Statistical analysis ....................................................................................................... 75
3.6 Analysis modelling ..................................................................................................... 76
3.7 Conclusions ................................................................................................................. 77
Chapter 4: APPLICATION OF COUPLED ANALYSES OF SEEPAGE AND STABILITY OF SLOPES SUBJECTED TO EXTREME RAINFALL ...... 78
4.1 Introduction ................................................................................................................. 78
4.2 Controlling factors of slope stability ........................................................................... 79
4.2.1 Rainfall data .................................................................................................... 79
4.2.2 Soil properties ................................................................................................. 79
4.3 Overview of analysis design ....................................................................................... 86
4.3.1 Analysis modelling ......................................................................................... 87
4.3.2 Parametric study .............................................................................................. 89
4.4 Results and discussion ................................................................................................ 97
4.4.1 The effect of rainfall events ............................................................................ 97
4.4.2 The effect of rainfall resolutions ................................................................... 103
viii
4.4.3 The effect of rainfall patterns ........................................................................ 109
4.4.4 The effect of soil properties .......................................................................... 112
4.5 Conclusions ............................................................................................................... 117
Chapter 5: APPLICATION OF COMPLEX ANALYSIS INCORPORATING SPATIAL VARIABILITY OF SOIL PROPERTIES .................................. 120
5.1 Introduction ............................................................................................................... 120
5.2 Slope stability analysis accounting for spatial variability of hydraulic conductivity 121
5.3 Determination of spatial variability parameters ........................................................ 122
5.4 Evaluation of slope stability accounting for variability of hydraulic conductivity .. 123
5.4.1 Analysis data ................................................................................................. 124
5.4.2 Modelling procedure .................................................................................... 125
5.4.3 Parametric study ............................................................................................ 127
5.5 Results and discussion .............................................................................................. 128
5.5.1 The effect of spatial variability of hydraulic conductivity on rainfall-induced slope failures for different slope inclinations ..................... 128
5.5.2 The effect of spatial variability of hydraulic conductivity on rainfall-induced slope failure for different soil hydraulic conductivities .... 136
5.5.3 The effect of spatial variability of hydraulic conductivity on rainfall-induced slope failures for general cases of slopes ........................... 147
5.6 Conclusions ............................................................................................................... 165
Chapter 6: SLOPE FAILURE PREDICTION ................................................................ 166
6.1 Introduction ............................................................................................................... 166
6.2 Overview of proposed approach ............................................................................... 168
6.3 Evaluation of existing methods for predicting rainfall-induced shallow slope failure ........................................................................................................................ 168
6.3.1 Evaluation of slope failure prediction method based on correlation between rainfall intensity and duration ........................................................ 168
6.3.2 Evaluation of slope failure prediction method based on antecedent rainfall .......................................................................................................... 171
6.3.3 Evaluation of slope failure prediction method based on rainfall IFD-soil interaction ..................................................................................................... 178
6.4 Rainfall thresholds for shallow slope failure ............................................................ 180
6.4.1 Rainfall thresholds based on rainfall intensity-duration ............................... 180
6.4.2 Rainfall thresholds based on antecedent rainfall .......................................... 184
6.4.3 Rainfall thresholds based on rainfall-soil interaction ................................... 188
ix
6.5 Development of a simple screening tool for anticipating slope failures ................... 191
6.6 Conclusions ............................................................................................................... 193
Chapter 7: CONCLUDING REMARKS .......................................................................... 195
7.1 Summary ................................................................................................................... 195
7.2 Extreme rainfall significantly contributes to rainfall-induced slope failures ........... 196
7.3 Rainfall events and soil properties are both primary controlling factors in rainfall-induced slope failures ................................................................................... 197
7.4 Spatial variability analysis is important when evaluating rainfall-induced failures of natural slopes ....................................................................................................... 197
7.5 Numerical analysis accounting for characteristics of rainfall events and unsaturated soil mechanics principles is necessary for more accurate and widespread use in rainfall-induced landslide prediction .......................................... 198
7.6 Recommendations for future work .......................................................................... 199
7.6.1 Slope stability analyses incorporating evaporation, transpiration, and root effects ............................................................................................................ 199
7.6.2 Evaluating rainwater infiltration using tipping bucket rain gauges .............. 199
7.6.3 Three-dimensional slope stability analyses .................................................. 199
7.6.4 Comprehensive spatial variability analyses .................................................. 199
7.6.5 Further development of a simple screening tool for landslide prediction .... 200
References ............................................................................................................................ 201
Appendices ........................................................................................................................... 216
Appendix A Calibration of the SVFLUX and SVSLOPE software .................................. 216
A.1 Seepage analyses ........................................................................................... 216
A.2 Slope stability analyses ................................................................................. 219
Appendix B Stability analyses .......................................................................................... 223
B.1 Methods of analysis ...................................................................................... 223
B.2 Modelling procedures of seepage analyses with SVFLUX (Thode and Gitirana, 2012) ........................................................................... 224
B.3 Modelling procedures of slope stability analyses with SVSLOPE (Fredlund et al., 2008) ...................................................................................233
Appendix C Shear box test results ....................................................................................236
x
LIST OF FIGURES
Figure Description Page
Figure 1.1 Worldwide slope failures triggered by rainfall in 2003-2010
(after Petley, 2014)
6
Figure 2.1 Seasonal variation in water table and pore pressure due to
climatic effects (after Wesley, 2010)
15
Figure 2.2 Typical SWCC for different types of soil (after Fredlund and
Xing, 1994)
16
Figure 2.3 Hydraulic conductivity function for unsaturated soils (after
Rahardjo et al., 2007)
17
Figure 2.4 Progress of infiltration through initially unsaturated soils
during rainfall (after Tholin and Kiefer, 1959)
18
Figure 2.5 Variability of a parameter illustrated by: (a) two different
coefficients of variation and (b) two different types of data
distribution (after Fenton and Griffiths, 2011)
37
Figure 2.6 Spatial variability of a parameter t in a slope geometry with
two different correlation lengths: (a) low correlation length
and (b) high correlation length (after Fenton and Griffiths,
2011)
39
Figure 2.7 Slope with two different correlation lengths: (a) low
correlation length and (b) high correlation length (after
Griffiths et al., 2007)
40
Figure 2.8 Thresholds of rainfall intensity-duration of landslide
occurrences obtained from many sites all over the world (after
Guzzetti et al., 2007)
44
Figure 2.9 Threshold line for landslide probability (after Crozier and
Eyles, 1980)
46
Figure 2.10 Cross-section of wetted zone of surficial soils due to rain
infiltration
47
Figure 2.11 The rainfall intensity-frequency-duration (IFD) curves for the
Jabiru site recorded at Gulungul Creek in 2007 (after Moliere
et al., 2007)
49
xi
Figure 2.12 The contribution of suction to shallow slope failures (after
Fourie, 1996)
50
Figure 3.1 Location of the Jabiru site 56
Figure 3.2 A characteristic Jabiru landslide: (a) front view and (b) side
view
57
Figure 3.3 Simplified geometry of the Jabiru slope 58
Figure 3.4 Location of the Sawyer’s Valley site 59
Figure 3.5 View of the Sawyer’s Valley site 60
Figure 3.6 Location of the Boddington site 61
Figure 3.7 Scheme of research procedures 62
Figure 3.8 Hourly extreme rainfall data obtained from Jabiru Airport
Station 014198, 24 to 28 February 2007 (Australian
Government, 2012)
64
Figure 3.9 Hourly extreme rainfall data obtained from Sembawang
Station 80, December 2006 (Singaporean Government, 2011)
65
Figure 3.10 Hourly extreme rainfall data obtained from Brisbane Station
040913, January 2011 (Australian Government, 2011)
65
Figure 3.11 Layout of soil-sampling and field tests at the Jabiru site 69
Figure 3.12 Layout of soil-sampling and field tests at the Sawyer’s Valley
site
69
Figure 3.13 Layout of soil-sampling and field tests at the Boddington site 70
Figure 3.14 The 1988 CSIRO Disc Permeameter used in field tests 71
Figure 3.15 The Eijkelkamp Tension Infiltrometer used in field tests 72
Figure 4.1 Particle size distribution of slope soils for: (a) the Jabiru site
and (b) the Sawyer’s Valley site
81
Figure 4.2 Plasticity of slope soils for: (a) the Jabiru site and (b) the
Sawyer’s Valley site
82
Figure 4.3 Soil-water characteristic curve (SWCC) for: (a) the Jabiru
site, (b) the Sawyer’s Valley site and (c) the Boddington site
85
Figure 4.4
Figure 4.5
Slope geometry and boundary conditions applied in the
seepage analysis
Hydraulic conductivity function (average values) of the soils
from the Jabiru, Sawyer’s Valley and Boddington sites
88
88
xii
Figure 4.6
Simulated rainfall with 64 mm/h major intensities occurring
every 20 h and various minor intensities with an average
value of 3.13 mm/h between the major intensities
93
Figure 4.7 Simulated rainfall with various intensities and time intervals
for major rainfall and constant intensity for minor rainfall
(I = 0.5 mm/h, much lower than ks)
94
Figure 4.8 Simulated rainfall with 24-h cyclic pattern occurring every
2 h and 0.5 mm/h minor intensity occurring between the
major intensities
95
Figure 4.9 Simulated rainfall with three different patterns: (a) delayed
pattern, (b) advanced pattern and (c) normal pattern (after
Rahimi et al., 2011 and Muntohar et al., 2013) (all the three
rainfall patterns had the same rainfall amount)
96
Figure 4.10 Stages for the effect of rainfall on slope instability 97
Figure 4.11 Variation in factors of safety after the start of rainfall for the
Jabiru slope with different rainfall intensity (all other material
parameters kept constant)
98
Figure 4.12 Variation in factors of safety after the start of rainfall for the
Jabiru slope with different rainfall volume in mm3/mm2 (all
other material parameters kept constant)
99
Figure 4.13 Variation in factors of safety for the Jabiru slope after the start
of rainfall data from February 2007 recorded at Jabiru Airport
(the closest station to the site), starting with different initial
suctions
101
Figure 4.14 Variation in pore-water pressures with time at 3 locations on
the slope (top, midway and toe) and at three different depths,
where position 3 is at the soil surface, position 1 at the base of
the weathered soil and position 2 midway between positions 1
and 3
101
Figure 4.15 Pore-water pressure contours at the surface soil for two
different rainfall time (t): (a) t = 6 h and (b) t = 84 h
102
xiii
Figure 4.16
Variation in factors of safety for the Jabiru slope using the
application of three rainfall data sets with various resolutions:
(a) Jabiru rainfall, (b) Singapore rainfall and (c) Brisbane
rainfall
104
Figure 4.17
Variation in factors of safety for the Jabiru slope with the
application of three simulated rainfall scenarios with various
resolutions (dt): (a) rainfall pattern with high intensity
fluctuation presented in Figure 4.5, (b) rainfall pattern with
medium intensity fluctuation presented in Figure 4.6(a) and
(c) rainfall pattern with slight intensity fluctuation presented
in Figure 4.7
108
Figure 4.18 Variation in factors of safety after the start of rainfall for the
Jabiru slope with various values of uniform rainfall intensities
110
Figure 4.19 Variation in factors of safety after the start of rainfall for the
Jabiru slope with various fluctuating intensities of rainfall
111
Figure 4.20 Variation in factors of safety after the start of rainfall for the
Jabiru slope with various rainfall patterns of smooth intensity
change
112
Figure 4.21 Variation in factors of safety after the start of rainfall for the
Jabiru slope with different hydraulic conductivity (all other
material parameters kept constant)
113
Figure 4.22 Variation in factors of safety after the start of rainfall for the
Jabiru slope with different initial suctions (all other material
parameters kept constant)
114
Figure 4.23 Variation in factors of safety after the start of rainfall for the
Jabiru slope with different apparent cohesion values (all other
material parameters kept constant)
115
Figure 4.24 Variation in factors of safety after the start of rainfall for the
Jabiru slope with different internal friction angles (all other
material parameters kept constant)
116
xiv
Figure 4.25 Variation in factors of safety after the start of rainfall for the
Jabiru slope with different actual shear strength parameters
obtained from laboratory tests for different soil samples (all
other material parameters kept constant)
116
Figure 5.1 Determination of correlation length of soil hydraulic
conductivity along: (a) Line 1 and (b) Line 2
124
Figure 5.2 Slope geometry, boundary conditions and variability of
hydraulic conductivity applied in the seepage analysis
126
Figure 5.3 Factor of safety of the Jabiru slope (β = ) with rainfall
time for various coefficients of variation (CV) of hydraulic
conductivity and different correlation lengths (θln k):
(a) θln k = m, (b) θln k = 3 m and (c) θln k = 5 m
130
Figure 5.4 Factor of safety of the Jabiru slope (β = ) with rainfall
time for various correlation lengths (θln k) of hydraulic
conductivity and different coefficients of variation (CV):
(a) CV = 10%, (b) CV = 100% and (c) CV = 1000%
131
Figure 5.5 Factor of safety of a steeper slope (β=30 ) with rainfall time
for various coefficients of variation (CV) of hydraulic
conductivity and different correlation lengths (θln k):
(a) θln k = m, (b) θln k = 3 m and (c) θln k = 5 m
132
Figure 5.6 Factor of safety of a steeper slope (β=30 ) with rainfall time
for various correlation lengths (θln k) of hydraulic conductivity
and different coefficients of variation (CV): (a) CV = 10%,
(b) CV = 100% and (c) CV = 1000%
133
Figure 5.7 The effect of different coefficients of variation of hydraulic
conductivity (CV) on seepage flow in the slope for the same
rainfall time and θln k = 1 with various CVs: (a) CV = 10%,
(b) CV = 100% and (c) CV = 1000%
135
Figure 5.8 The effect of different correlation lengths (θln k) of hydraulic
conductivity on seepage flow in the slope for the same rainfall
time and CV = 500% with various θln k: (a) θln k = 1,
(b) θln k = 3 and (c) θln k = 5
135
xv
Figure 5.9 Factor of safety of the Jabiru slope (ks = 80 mm/h) with
rainfall time for various coefficients of variation (CV) of
hydraulic conductivity and different correlation lengths (θln k):
(a) θln k = m, (b) θln k = 3 m, and (c) θln k = 5 m
137
Figure 5.10 Factor of safety of the Jabiru slope (ks = 80 mm/h) with
rainfall time for various correlation lengths (θln k) of hydraulic
conductivity and different coefficients of variation (CV):
(a) CV = 10%, (b) CV = 100%, and (c) CV = 1000%
138
Figure 5.11 Seepage propagation in the low-conductivity slope (average
ks = 8 mm/h and β = ) with high spatial variability of
hydraulic conductivity (CV = 000% and θlnk = 1 m) exposed
to uniform rainfall intensity (I = 8 mm/h) from rainfall time:
(a) t = 0 h to (b) t = 48 h and (c) t = 96 h
139
Figure 5.12 Seepage propagation in the high-conductivity slope (average
ks = 80 mm/h and β = ) with high spatial variability of
hydraulic conductivity (CV = 000% and θlnk = 1 m) exposed
to uniform rainfall intensity (I = 8 mm/h) from rainfall time:
(a) t = 0 h to (b) t = 48 h and (c) t = 96 h
140
Figure 5.13 Variation in factors of safety with rainfall time for two
different slope inclinations and various coefficients of
variation for each slope inclination
141
Figure 5.14 Variation in factors of safety with rainfall time for two
different values of hydraulic conductivity and various
coefficients of variation for each slope inclination
142
Figure 5.15 The effect of spatial variability of hydraulic conductivity on
the amount of runoff in low-conductivity (ks = 8 mm/h) and
high-conductivity (ks = 80 mm/h) slopes exposed to rainfall
for deterministic (det) and spatial variability (sv) analyses
143
xvi
Figure 5.16 The effect of spatial variability of hydraulic conductivity on
pore-water pressures generated along the layer intercept (2 m
deep) in low-conductivity (ks = 8 mm/h) and high-
conductivity (ks = 80 mm/h) slopes after 90-hour rainfall (I =
8 mm/h) for deterministic (det) and spatial variability (sv)
analyses (the effect is also shown for slopes without an
impermeable layer at shallow depths, called ‘deep’ for
comparison)
144
Figure 5.17 Minimum factors of safety (Fm) of the Jabiru slope with
various spatial variability of hydraulic conductivity for:
(a) ks = 8 mm/h and β = , (b) ks = 8 mm/h and β=30 and
(c) ks = 80 mm/h and β=
146
Figure 5.18 Factor of safety of slopes (β = ) with rainfall time
resulting from deterministic (det) and spatial variability (sv)
analyses for 3 different soil stratifications: impermeable layer
at a shallow depth (imp), heterogeneous slopes (het) and
homogeneous slopes (hom)
149
Figure 5.19 Pore-water pressure contours of slopes (ks = 8 mm/h and
β = ) resulting from deterministic analyses at t = 90 h for:
(a) slopes with impermeable layer at a shallow depth, (b)
heterogeneous slopes and (c) homogeneous slopes
150
Figure 5.20 Pore-water pressure contours of slopes (ks = 8 mm/h and β =
) resulting from spatial variability analyses at t = 90 h for:
(a) slopes with impermeable layer at a shallow depth,
(b) heterogeneous slopes and (c) homogeneous slopes
151
Figure 5.21 Pore-water pressures along the base of surface soils (at 2 m
depth) resulting from deterministic analyses with different
time after the start of rainfall for shallow inclination slopes
(β = ) with 3 different soil stratifications: (a) impermeable
layer at a shallow depth (imp-19-det), (b) heterogeneous
slopes (het-19-det) and (c) homogeneous slopes (hom-19-det)
153
xvii
Figure 5.22 Pore-water pressures along the base of surface soils (at m
depth) resulting from spatial variability analyses with
different time after the start of rainfall for shallow inclination
slopes (β = ) with 3 different soil stratifications:
(a) impermeable layer at a shallow depth (imp-19-sv), (b)
heterogeneous slopes (het-19-sv) and (c) homogeneous slopes
(hom-19-sv)
154
Figure 5.23 Factor of safety of slopes (β = 40 ) with rainfall time
resulting from deterministic (det) and spatial variability (sv)
analyses for 3 different soil stratifications: impermeable layer
at 2 m depth (imp), heterogeneous slopes with less permeable
layer at 2 m depth (het) and homogeneous slopes (hom)
155
Figure 5.24 Pore-water pressure contours of slopes (ks = 8 mm/h and
β = 40 ) resulting from deterministic analyses at t = 90 h for:
(a) slopes with impermeable layer at 2 m depth,
(b) heterogeneous slopes with less permeable layer at 2 m
depth and (c) homogeneous slopes
156
Figure 5.25 Pore-water pressure contours of slopes (ks = 8 mm/h and
β = 40 ) resulting from spatial variability analyses at t = 90 h
for: (a) slopes with impermeable layer at 2 m depth,
(b) heterogeneous slopes with less permeable layer at 2 m
depth and (c) homogeneous slopes
157
Figure 5.26 Pore-water pressures resulting from deterministic analyses
along slope base (β = 40 ) at 2 m depth of surface soils
(ks = 8 mm/h) with different rainfall time for 3 different soil
stratifications: (a) impermeable layer at 2 m depth, (b)
heterogeneous slopes (less permeable layer at 2 m depth), and
(c) homogeneous slopes
158
Figure 5.27 Pore-water pressures resulting from spatial variability
analyses along slope base (β = 40 ) at 2 m depth of surface
soils (ks = 8 mm/h) with different rainfall time for 3 different
soil stratifications: (a) impermeable layer at a shallow depth,
(b) heterogeneous slopes and (c) homogeneous slopes
159
xviii
Figure 5.28 Factor of safety of slopes with rainfall time resulting from
deterministic (det) analyses for 2 different inclinations:
β = and 40 , and 3 different soil stratifications:
impermeable layer at a shallow depth (imp), heterogeneous
slopes (het) and homogeneous slopes (hom)
160
Figure 5.29 Factor of safety of slopes (β = ) with rainfall time
resulting from spatial variability (sv) analyses for different
inclinations: β = and 40 , and 3 different soil
stratifications: impermeable layer at a shallow depth (imp),
heterogeneous slopes (het) and homogeneous slopes (hom)
161
Figure 5.30 Pore-water pressure contours for slopes (ks = 8 mm/h and
β = 40 ) with impermeable layer at a shallow depth resulting
from deterministic analyses at: (a) t = 90 h and (b) t = 120 h
and spatial variability analyses at: (c) t = 90 h and (d) t = 120h
162
Figure 5.31 Pore-water pressure contours for heterogeneous slopes
(ks = 8 mm/h and β = 40 ) resulting from deterministic
analyses at: (a) t = 90 h and (b) t = 120 h and spatial
variability analyses at: (c) t = 90 h and (d) t = 120 h
163
Figure 5.32 Pore-water pressure contours for homogeneous slopes
(ks = 8 mm/h and β = 40 ) resulting from deterministic
analyses at: (a) t = 90 h and (b) t = 120 h and spatial
variability analyses at: (c) t = 90 h and (d) t = 120 h
164
Figure 6.1 Factor of safety for the Jabiru slope, with rainfall duration for
various rainfall intensities, and in-situ slope parameters
(ks = 8 mm/h and β = )
169
Figure 6.2 Threshold of rainfall intensity-duration triggering slope
failure (ks = 8 mm/h and β = ), where in this case ‘failure’
is deemed to be the minimum factor of safety achieved, i.e.
Fm = 1.1
170
Figure 6.3 Factor of safety for the Jabiru slope, with rainfall time related
to various initial suction measures (ks = 8 mm/h and β = )
171
Figure 6.4 Hypothetical variation of factor of safety with rainfall time
resulting from a slope stability analysis
172
xix
Figure 6.5 The effect of various combinations of antecedent and main
rainfall on slope stability for ks = 8 mm/h and β =
174
Figure 6.6 Rainfall threshold line for slope failure for ks = 8 mm/h and
β =
174
Figure 6.7 Rainfall threshold line for slope failure with ks = 8 mm/h and
β = verified by several rainfall events
177
Figure 6.8 The effect of various rainfall patterns and events on the
stability of the slope with ks = 8 mm/h and β =
178
Figure 6.9 Rainfall threshold for shallow slope failure (the Jabiru slope)
based on the modified rainfall IFD method
179
Figure 6.10 Comparison of rainfall time required to develop a wetting
front with various initial suction levels using both the
approximation and numerical methods of analysis
180
Figure 6.11 Rainfall threshold lines for slope failure based on rainfall
intensity-duration for a slope with two different hydraulic
conductivities (ks = 8 mm/h and 80 mm/h) and three slope
inclinations: (a) β = , (b) β = 30 and (c) β = 40
182
Figure 6.12 ainfall threshold lines for slope failure based on rainfall
intensity-duration for a slope with three inclinations (β = ,
30 , and 40 ) and two different hydraulic conductivities: (a)
ks = 8 mm/h and (b) ks = 80 mm/h
183
Figure 6.13 Rainfall threshold lines for slope failure based on antecedent
rainfall for a slope with two different hydraulic conductivities
(ks = 8 mm/h and 80 mm/h) and three slope inclinations: (a) β
= , (b) β = 30 and (c) β = 40
185
Figure 6.14 ainfall threshold lines for slope failure based on antecedent
rainfall for a slope with three different slope inclinations (β =
, 30 , and 40 ) and two different hydraulic conductivities:
(a) ks = 8 mm/h and (b) ks = 80 mm/h
187
Figure 6.15 Monitoring system using a simple screening tool to anticipate
slope failures
192
xx
LIST OF TABLES
Table Description Page
Table 2.1 Static equilibrium conditions satisfied by limit equilibrium
methods (after Abramson et al., 2002)
29
Table 2.2 The coefficients of variation for various soil properties (after
Lacasse and Nadim, 1996)
41
Table 2.3 Statistics of the hydraulic conductivity of compacted soil
liners (after Benson, 1993)
42
Table 3.1 Detailed activities during preparation phase 63
Table 3.2 Overview of data collection 66
Table 3.3 General overview of analysis modelling 67
Table 4.1 Basic and index soil properties and classifications 83
Table 4.2 Data analysis of hydraulic conductivity tests using the Disc
Permeameter or Tension Infiltrometer
84
Table 4.3 Shear strength parameters resulting from shear box test 86
Table 4.4 Summary of various parameters applied in parametric study 91
Table 4.5 Summary of variations of simulated rainfall with fluctuating
intensity
95
Table 5.1 Statistical parameters regarding the spatial variability of
hydraulic conductivity of slopes at the Sawyer’s Valley site
123
Table 5.2 Constant input parameters used in the analysis 127
Table 5.3 Variation of input parameters used in the analysis 128
Table 6.1 Determination of antecedent daily rainfall factors (Kn) based
on uniform rainfall intensity (I = 43 mm/day)
173
Table 6.2 Antecedent daily rainfall factors (Kn) for different hydraulic
conductivities (ks) and angles of slope inclination (β)
173
Table 6.3 Combination of antecedent and main rainfall for determining
the threshold line for slope failure (ks = 8 mm/h and β = )
174
Table 6.4 Various patterns of simulated rainfall and rainfall data used
for verification in slope failure probability (Pn refers to a
rainfall event that n-day before the main rainfall (P0))
176
Table 6.5 Risk analyses for the Jabiru slope 188
xxi
LIST OF SYMBOLS
a SWCC parameter
AE actual evaporation
B point where soil sampling and field tests were carried out in the
Boddington site
CP percentage of coarse particles
CV coefficient of variation
C(ψ) correction function for SWCC equation
c parameter for landslide prediction
c′ apparent cohesion
cp′ peak apparent cohesion
cr′ residual apparent cohesion
D rainfall duration
dt rainfall resolution
e the natural number
F factor of safety
Fi initial factor of safety
Fm minimum factor of safety
Fn factor of safety for slopes on the nth day before main rainfall
FP percentage of fine particles
h water tension
ht hydraulic or total head
I rainfall intensity
Imin minimum infiltration
IFD intensity-frequency-duration of rainfall
K factor indicating contribution of rainfall to antecedent rainfall index
Kn factor indicating contribution of antecedent rainfall on the nth day
before main rainfall
klim limiting value for saturated hydraulic conductivity required to saturate
surficial soils
kmin minimum hydraulic conductivity
ks saturated hydraulic conductivity
kw unsaturated hydraulic conductivity
xxii
L landslide
LL liquid limit
m SWCC parameter
mw slope of SWCC
n number of data
n SWCC parameter
NL no landslide
NP net percolation
Nx number of grids in X axis
Ny number of grids in Y axis
P point where soil sampling and field tests were carried out in the
Sawyer’s Valley site
P precipitation
P rainfall volume
p power factor adjusting prediction of infiltration
P0 main rainfall (volume)
Pa0 antecedent daily rainfall index
Pn antecedent rainfall on the nth day before main rainfall
PI plasticity index
PL plastic limit
Q infiltration discharge
q applied boundary flux
R infiltration rate under steady-state condition
Roff runoff
r inner radius of the water tower for tension infiltrometer and disc
permeameter
S wetting front capillary suction
S point where soil sampling and field tests were carried out in the Jabiru
site
SG specific gravity
T analysis duration
t elapsed rainfall time
Tw time required to saturate wetted zone
ua pore-air pressure
xxiii
uw pore-water pressure
(ua – uw) matric suction
V rainfall volume
vi infiltration rate
w water content
zw depth of wetting front
α infiltration factor
α parameter for landslide prediction
α slope angle
β parameter for landslide prediction
β slope angle
ϕ′ internal friction angle
ϕp′ peak internal friction angle
ϕr′ residual internal friction angle
ϕb the angle indicating the rate of increase in shear strength relative to
matric suction
μ change of volumetric water content from initial condition
μ mean value
σ normal stress
σ standard deviation
σn total normal stress
σ2 variance
(σ – ua) net normal stress
τ distance
τ shear strength
ψ matric suction
ψi initial suction
θ correlation length
θ volumetric water content
θs saturated volumetric water content
θw unsaturated volumetric water content
Θ normalized volumetric water content
γt total unit weight of soil
xxiv
γw unit weight of water
ρ bulk density
ρ correlation estimator
1
CHAPTER 1
INTRODUCTION
1.1 RESEARCH BACKGROUND
Rainfall-induced slope failures occur frequently all over the world during rainy
seasons. These types of slope failures have become one of the most disastrous natural
hazards worldwide (Alcantara-Ayala, 2002), usually causing economic loss and
sometimes even fatalities. These failures commonly occur in natural slopes,
particularly residual soil slopes (Campbell, 1975; Lumb, 1975; Morgestern and de
Matos, 1975; Fukuoka, 1980; Brand et al., 1984; Vargas et al., 1986; Kim et al., 1991;
Lacerda, 1997; Au, 1998; Franks, 1999; Rahardjo et al., 2009) and as infrastructure
develops around slopes the risk of damage to such infrastructure due to slope failure
increases. It is noted that the majority of rainfall-induced slope failures occur through
shallow failure mechanisms (Guzzetti et al., 2008), with the depth of failure usually
less than 2 m.
Both rainfall and soil properties have been widely accepted as primary controlling
factors in rainfall-induced slope failures (Brand et al., 1984; Rahardjo et al., 2007),
particularly in tropical regions where there are often high intensity rainfall and humid
conditions (i.e. low evaporation rates). Rainfall can also cause intense and deep
chemical weathering of slopes and the leaching of minerals from near-surface soils.
This may result in open structures of the soils near the slope surface, with high void
ratios not uncommon. Hydraulic properties of a soil, which are related to void ratio
2
(among other factors) determine the amount of rainwater infiltrating to slopes; this
infiltration may trigger slope failures.
Rainfall events, which may be quantified in terms of intensity, duration, antecedent
condition, resolution, and pattern play an important role in rainfall-induced slope
failure, as suggested by researchers such as Brand et al. (1984), Fourie (1996),
Rahardjo et al. (2001; 2007), Hearman and Hinz (2007), Rahimi et al. (2011) and
Muntohar et al. (2013). Intense rainfall has often been identified as a triggering factor
for many slope failures around the world (Fuchu et al., 1999; van Asch et al., 1999;
Olivares and Picarelli, 2003; Shaw-Shong, 2004; Huat et al., 2006; Guzzetti et al.,
2008) and it is accepted that there have been many slope failures during prolonged
rainfall (Petley, 2012). It is well recognised that antecedent rainfall significantly
contributes to rainfall-induced failures of low-conductivity slopes, but probably has
less significant contribution to those of high-conductivity slopes (Rahardjo et al.,
2008). As rainfall intensity usually fluctuates, rainfall resolution is often crucial in
determining the amount of rainwater infiltration which may lead to slope failures.
Thus, the use of high resolution rainfall data (hourly, rather than daily rainfall data) in
the analysis of rainfall-induced slope stability may produce more accurate results, as
suggested by Hearman and Hinz (2007) and Lowry et al. (2009). In addition, specific
rainfall patterns (high intensities in the beginning, followed by a consistent decrease
towards the end of the rainfall) produced the worst slope stability, the lowest
minimum factor of safety (indicator of slope stability) and the shortest time to reach
the minimum factor of safety (Rahimi et al., 2011; Muntohar et al., 2013).
The interaction between rainfall events and soil hydraulic properties essentially
determines the amount of rainwater infiltration required to reduce suction of surficial
soil, which can trigger a slope failure. Theoretically, the incident rainfall can be
totally infiltrated to soils when the rainfall intensity is about the same magnitude as
the soil hydraulic conductivity. In this case, rainwater infiltration is most likely to
reduce suction of the surficial soil to a critical condition. Rainfall with very low
intensity will infiltrate completely to the surficial soil but it may be insufficient to
reduce suction of the soil. In contrast, when rainfall has very high intensity, rainwater
will transfer partly to runoff. Thus, rainwater infiltration may also be insufficient to
reduce suction of the soil because intense rainfall is usually of shorter duration.
3
In recent decades here has been a change in the understanding of how rainfall-induced
shallow slope failure occurs. Initially, the mounding of groundwater tables in high
hydraulic conductivity soils and artesian uplift pressure in surface soils in low
hydraulic conductivity soils (Deere and Patton, 1971), were assumed to trigger
rainfall-induced slope failure, and were usually associated with deep-seated failure
mechanisms. These assumptions ignored matric suction above the water table. As a
result of investigations of slope failures in residual soils of Hong Kong for the period
of 1950 – 1973 (Lumb, 1975), rainwater infiltration was identified as the primary
cause of slope failures, rather than seepage from below. This is confirmed by the fact
that many residual soil slopes with deep groundwater tables and inclination angles
greater than the repose angle remain stable during the dry season, but fail when the
slopes are subject to prolonged intense rainfall. In these cases, the contribution of
matric suction to shear strength cannot be ignored (Fredlund and Rahardjo, 1993).
While matric suction increases the strength of unsaturated soils, this strength
decreases significantly as rainwater infiltrates the surficial soil of the slope.
A number of studies have confirmed that matric suction plays a key role in shallow
slope failures (Pradel and Raad, 1993; Rahardjo et al., 1995; Au, 1998; Fourie et al.,
1999). As rainwater infiltrates the slope surface, matric suction decreases and the
wetting front moves down until reaching a critical depth where the shear strength of
the soil cannot maintain slope stability (Fourie, 1996). This type of failure is more
likely to occur in slopes with relatively low hydraulic conductivity, than those with
high hydraulic conductivity, such as clean sands (Pradel and Raad, 1993). In the
former case, infiltration can only reduce suction of the surficial soils to shallow
depths, leading to a shallow slope failure mechanism.
Accepting that rainfall and soil properties are the controlling factors, coupled analyses
of seepage and slope stability are now commonly performed to evaluate rainfall-
induced slope instability. The deterministic approach is a common practice in both
seepage and slope stability analyses. In this approach, homogeneous (in terms of both
shear strength and hydraulic properties) slope soils are usually assumed, to simplify
the analysis problem. However, soils are rarely homogeneous in-situ and tend to be
spatially variable due to the changeable nature of soil formation (Vanmarcke, 1977a).
As a result, rainfall may produce infiltration rates at the site that are different from
simulations that assume soil homogeneity.
4
To overcome the limitations of slope stability assessments that assume soil
homogeneity, pre-defined failure plane and inter-slice forces, finite element and
probabilistic methods have been increasingly employed in slope stability analyses
(Fenton and Griffiths, 2005; Griffiths et al., 2011). With the finite element method, it
is possible to incorporate spatial variability of soil properties in seepage and stability
analysis of a slope. The probabilistic approach for slope stability analysis commonly
ignores spatial correlation of soil variability. Vanmarcke (1977a) indicated that in-
situ soil properties are inherently spatially variable. Christian (2004) suggested that
hydraulic conductivity was most variable (coefficient of variation, CV up to 767 %)
among engineering properties of soil. However, practicing engineers still rarely take
account of spatial variability of hydraulic conductivity, to avoid the increase of
complexity in the analysis due to nonlinearity of soil hydraulic properties, including
hydraulic conductivity. Moreover, other sources of soil variability can be minimised
by improved soil sampling, testing, and analysis. Spatial variability analysis may be
important for simulating in-situ soil properties, particularly hydraulic conductivity, to
evaluate the stability of slopes exposed to rainfall.
Due to the uncertainty of rainfall-induced failure of natural slopes, prediction of the
typical shallow slope failures is necessary to anticipate its consequences. Many
studies have established techniques for predicting landslide probability, starting from
traditional techniques (Vaughan, 1985; Nunes et al., 1989; Senanayaka et al., 1994) to
more quantitative approaches. Probably the most common method used to predict
rainfall-induced landslides in many different countries was the use of empirical
correlation between intensity and duration of rainfall leading to landslides (e.g. Caine,
1980; Kim et al., 1991; Larsen and Simon, 1993; Corominas et al., 2003; Guzzetti et
al., 2007; Dahal and Hasegawa, 2008). Another interesting approach to defining
rainfall threshold of landslide probability was illustrated by Crozier and Eyles (1980).
This approach was established based on empirical correlation between antecedent
conditions and a particular rainfall event leading to landslides. Both approaches rely
on landslide occurrences in the past and do not explicitly account for soil properties.
The significant role of both rainfall and soil properties was clearly indicated by Brand
et al. (1984) through a study on typical characteristics of slope failures in Hong Kong,
and Rahardjo et al. (2007) based on investigations of slope failures in Singapore.
5
It is now widely accepted that shallow slope failure mechanisms are triggered by
infiltration of rainwater to surficial soils as described previously. This mechanism has
been used to establish an approximate method to predict landslide probability based
on statistical rainfall data and soil properties (Pradel and Raad, 1993; Fourie, 1996).
However, this approximate method tends to produce conservative results because of
inherent simplifications.
The purpose of this chapter is to outline the importance of this project, the aim and
scope of this research, and provide an overview of the thesis.
1.2 OCCURRENCES OF SLOPE FAILURES TRIGGERED BY RAINFALL
Slope failures have become one of the most frequent natural hazards all over the
world, even recorded as the highest frequency in America for period of 1990-1999
(Alcantara-Ayala, 2002) among the other most frequent natural hazards such as
storms, volcano, earthquake, flood, and tsunami. In the period of 2003-2010,
worldwide rainfall-induced slope failures had generally shown an increasing trend
and 2010 was a bad year as shown in Figure 1.1. There were 6211 deaths recorded for
494 slope failures triggered by rainfall in 2010. The largest event in terms of lives lost
was the Gansu landslide in China on the 8th August, which killed 1765 people. Other
very large events were the 2nd March Bududa landslide, Uganda (358 deaths), the 6th
April Morrao de Bubma landslide in Niteroi, Brazil (196 deaths), the 7th August
debris flows in Leh, India (234 deaths); and the 4th October Wasior landslide in West
Papua, Indonesia (145 deaths).
6
Figure 1.1 Worldwide slope failures triggered by rainfall in 2003-2010 (after Petley,
2014)
In several regions, slope failures are commonplace and the occurrences occasionally
cause tremendous losses. For example, rainfall-induced slope failures in the Nepal
Himalaya region have caused huge damage to lives, property, infrastructure, and
environment particularly in the monsoon season (Dahal, 2012). The Nepal Himalayan
region is one of the most vulnerable zones of worldwide landslides, constituting about
30% of the world’s total landslide-related damage value (Li, 1990). A series of
landslides have occurred in this region with huge losses. For example, 50 people were
killed by landslides (in the half monsoon, 10 June – 15 August 2009) in Nepal. In
1988, a huge landslide at Darbang about 200 km west of Kathmandu, killed 109
people and temporarily blocked the Myagdi River. About 62 years before this
incident, a landslide had buried Darbang area, killing 500 people (Yagi et al., 1990).
This was the worst landslide disaster in the history of the Himalayan landslides.
Another landslide tragedy took place at Malpa Uttarakhand, India on 11 and 17
August 1998 resulting in the deaths of 380 people when massive landslides washed
away the entire village. Apart from such huge landslides, many small-scale landslides
were unreported when they occurred in remote areas of the Himalayas. Moreover, the
loss of productive lands in the hills due to landslides and related mass erosion
phenomena during rainy seasons, which are seldom reported unless they involve the
loss of life, seems to be so great that the economic loss, if quantified, would be no less
than that during any other big natural disasters. National infrastructures such as roads,
7
bridges, dams, hydropower stations, canals and buildings repeatedly suffer landslide
and flood damages. Similarly, due to a rapid increase in population over the
Himalayan hills in the last three decades, the landslides continuously cause
considerable loss of life, property, and significant damage to the vital economic
system of the nations in the Himalayan Region.
China is possibly another country with extremely serious geological disasters
including landslides triggered by rainfall. Every year, the direct economic losses of
geological disasters account for over 20% of the total losses from all natural disasters.
Nationwide, the landslide related direct and indirect economic losses account for
more than 20 billion Yuan (approximately 2 billion EUR) every year (Hu and Tang,
2005; Bai et al., 2011). According to the inventories of the China Institute of Geo-
Environment Monitoring, there were a total number of 102,804 geological disasters
nationwide in 2006, of which 86% were landslides. In 2007 there were 25,364 entries
nationwide, of which 61% were landslides. In 2008, 14,350 landslides were recorded
from a total number of 26,580 geological disasters, which accounts for 54%. In the
past 10 years, several large landslide disasters occurred. For example, the Gansu
landslide which took place on August 8, 2010, caused massive fatalities as mentioned
previously. These numbers underline the importance of disaster prevention and relief
for the reduction of economic losses. Therefore, landslide risk mapping and scientific
predictions are critical for disaster management agencies worldwide.
In Japan, many recurring rainfall-induced landslides occurred during heavy rains over
the last 65 years, resulting in a total of more than 1000 casualties over the last 65
years (Chigira, 2001). Such recurring disasters are possible because the weathered
granite had the potential for repeated landslides since the failures exposed rock
having low shear strength and the depth of weathering stages could be long-standing
erosion base levels (Durgin, 1977). Such fast weathering phenomena and repeated
failure on granitic terrain was also studied by Chigira and Ito (1999) on artificial cut
slopes in Japan. In 2004, very intense rainfall (the highest rainfall in the previous 30
years) triggered more than 300 landslides in Moriyuki and Monnyu catchment area,
Shikoku Island of Japan (Dahal et al., 2008). Field observations indicated that the
slides occurred mainly in residual soils on forested or partly forested slopes. Most of
the slides were shallow and translational in nature with the failure surface located
along the contact between overlying residual soil and relatively less weathered
8
bedrock at varying depths. Not only in Japan, but also in other granitic terrains of the
humid and tropical regions, shallow failure phenomena are very common. In granite
and gneiss areas of Rio de Janeiro in 1966 and 1967, severe rainstorms resulted in
tens of thousands of landslides and about 1000 casualties (Durgin, 1977). During the
main rainfall months of May to September in Hong Kong, numerous landslides occur
in cut and natural slopes of soils formed by the residual soils over granite and
granodiorite of Jurassic to cretaceous age (Irfan, 1998; Dai et al., 2003). Moreover,
two thirds of the land area of the Korean peninsula is composed of soils formed by
weathered products of granite and gneiss. During heavy rainfall, many slope failures
in these weathered rocks are characterized by relatively shallow failure surfaces
(typically 2-3 m in depth) that develop parallel to the original slope (Kim et al.,
2004). Southern Italy has also suffered from landslides in weathered granite
(Calcaterra et al., 1996). A great number of landslides (2560 events) during 55 years
(1950-2005) were compiled through a thorough literature search worldwide and the
dominant modes of the landslides were recorded as shallow landslide (52.8%) and
debris flow (42.2%) (Guzzetti et al., 2008). Therefore, this thesis focused on shallow
landslide mechanisms triggered by rainfall.
Farahmand and Aghakouchak (2013) indicated that landslides cause thousands of
casualties and billions of dollars in damages across the world every year. According
to the US Geological Survey (USGS), landslides result in tens of deaths and over 1-2
billion USD in property damages (USGS, 2006) annually. For example, the Western
US has suffered from several storm-triggered landslides during the El-Nino seasons
of 1982-1983, resulting in millions of dollars in loss (Spiker and Gori, 2003; Hong et
al., 2006b). In several other landslide events, thousands of people died and
disappeared within a few minutes/hours, e.g. 1999 landslide in Vargas, Venezuela
(Larsen et al., 2000). Landslides in South-east Asia are also one of the most
widespread disasters mainly because of the climate condition, mountainous terrain
and socioeconomic conditions (Apip et al., 2010). For instance, in 2006, after a period
of heavy rainfall, a series of landslides on Leyte Island, Philippines caused over 1000
fatalities (Sassa et al., 2010) and the 4th October 2010 Wasior landslide in West
Papua, Indonesia claimed 145 lives.
9
1.3 AIMS AND SCOPE OF THE RESEARCH
This thesis investigates the effect of the main controlling factors on the mechanisms
of rainfall-induced shallow slope failures. In particular, the effect of spatial variability
of soil hydraulic properties on the slope failure is taken into consideration to account
for the in-situ condition of natural slopes. Risk analysis was also carried out to
develop insight into how the likelihood of slope failures can be determined,
particularly the mechanism and consequences of rainfall-induced shallow slope
failure.
Numerical modelling was used to carry out coupled analyses of seepage and slope
stability using the commercially available software SVFLUX and SVSLOPE. The
finite element method was employed to incorporate complex analysis modelling for
more visual and accurate results. The spatial variability method was specifically
utilised to take account of the inherent soil variability closer to in-situ condition for
more realistic results. All the analyses were referred to a landslide occurrence in 2007
at the Jabiru site in the Northern Territory, Australia. This study highlighted the effect
of soil hydraulic properties as controlling factors on rainfall-induced shallow
landslides.
In order to achieve the overall aim of this research, research areas were summarized
as follows:
1. The first area of research uses the landslides that occurred at Jabiru in the
Northern Territory, Australia in 2007 to develop insights into how the main
controlling factors, particularly soil hydraulic properties, such as initial suction,
hydraulic conductivity, soil water characteristic curve, and unsaturated shear
strength properties, and rainfall events (in terms of intensity, duration, resolution,
and pattern) determine the shallow failure mechanism of rainfall-induced slopes.
Parametric studies were carried out to cover not only the specific case of the
Jabiru landslide but also general cases of rainfall-induced shallow slope failures
possibly occurring in any other sites.
2. The second area of research investigated the effect of spatial variability of soil
hydraulic conductivities on rainfall-induced shallow slope failure mechanisms.
Another, more accessible site, Sawyer’s Valley, which is near Perth, was chosen
10
for collecting data from field tests required to characterise the spatial variability of
hydraulic conductivities. Spatial variability parameters, such as correlation length,
indicating correlation levels (strong or weak) of soil properties with distance, and
coefficient of variation, indicating distribution of soil properties variation, were
used to model variability of soil hydraulic properties in the slope that are more
representative of the in-situ condition, and the importance of this factor in the
evaluation of rainfall-induced failures.
3. The last area of research in this thesis investigated approaches to predict the
likelihood of rainfall-induced shallow slope failures based on soil hydraulic
properties and antecedent conditions. New approaches for risk analysis were
developed to determine rainfall thresholds of the probability of slope failures
based on existing approaches. This analysis could be used as a screening tool for
the slope failure probability based on rainfall data and unsaturated soil mechanics
principles.
1.4 THESIS OUTLINE
This thesis focuses on three research areas as follows:
1. Numerical modelling of controlling factors in the analyses of rainfall-induced
slope failures.
2. Spatial variability analyses of rainfall-induced slope failures.
3. Prediction of rainfall-induced slope failures.
The structure of the thesis reflects the three primary topics above and it is presented in
seven chapters. In the current chapter, the background, aims, scope and outline of the
research are presented. The literature review is presented in Chapter 2 to provide a
background to subsequent chapters. The characteristics of tropical residual soils in
natural slopes, the contribution of the controlling factors on slope instability, failure
mechanisms, seepage and slope stability analyses of rainfall-induced slopes, and
approaches for predicting rainfall-induced shallow slope failures, are reviewed.
The research methodology is discussed in Chapter 3. Site characteristics are described
and general modelling for seepage and stability analysis of rainfall-induced slopes are
presented.
11
Chapter 4 examines the effect of the main controlling factors of rainfall-induced
shallow slope failures. Parametric studies regarding the effect of the controlling
factors on the slope instability, such as rainfall events (in terms of intensity, duration,
resolution, pattern, and antecedent event), hydraulic conductivity, soil water
characteristic curve, and unsaturated shear strength parameters, were carried out.
Deterministic analyses were performed and discussed with respect to the Jabiru
landslide to provide a better understanding of the mechanisms involved in rainfall-
induced shallow slope failure. Results obtained from deterministic analyses based on
the limit equilibrium method (the most common analysis of slope stability) in this
chapter are used as benchmark for those in the next chapters.
Spatial variability analysis was performed in Chapter 5 to investigate the effect of soil
properties, particularly hydraulic conductivity and randomly distributed spatial
variables, on slope instability. The spatial variability of the soil hydraulic
conductivities was applied based on soil parameters determined from field
investigations. The results of this chapter are compared with the results of
deterministic analysis method presented in the previous chapter.
Risk analysis was performed in Chapter 6 to examine the likelihood of slope failure.
This analysis provides invaluable information for taking actions including early
warning to avoid the consequences of a slope failure.
Finally, concluding remarks and recommendations for future studies are presented in
Chapter 7. All key points described previously in the main body of the thesis were
briefly discussed in this closing chapter. The chapter highlights the main points of the
three study areas and points to new questions reflected from results of the thesis for
further research.
1.5 PUBLICATIONS
Publications based on this thesis are as follows:
Suradi, M., Fourie, A., Beckett, C., and Buzzi, O. (2014). Rainfall-induced landslides:
development of a simple screening tool based on rainfall data and unsaturated soil
mechanics principles. Proceedings of the Sixth International Conference on
Unsaturated Soils, 1-4 July, Sydney, Australia, 1459-1465.
12
Suradi, M., and Fourie, A. (2014). The effect of rainfall patterns on the mechanisms
of shallow slope failure. Aceh International Journal of Science and Technology, 3(1):
1-18.
Suradi, M., Fourie, A., and Saynor, M.J. (2014). Rainfall-induced landslides: lessons
learned from an extreme rainfall event in northern Australia. Landslides (submitted).
13
CHAPTER 2
LITERATURE REVIEW
2.1 INTRODUCTION
Rainfall-induced slope failures have become a crucial issue in many countries all over
the world. Numerous failures have occurred in natural slopes, particularly those of the
residual soil-type, and shallow slope failure appears to be the most common
occurrence. Slope failure mechanisms appear to be related to engineering properties
that are inherent to residual soils (Fourie, 1997; Maail et al., 2004; Wesley, 2010).
Coupled analyses of seepage and slope stability are usually performed to evaluate the
level of risk regarding a slope failure. The deterministic approach, common practice in
seepage and slope stability analyses will be reviewed here as the theoretical basis for
the main analyses in the following chapters, and also used as a benchmark in this
study. The finite element approach is specifically considered in seepage analysis in
order to account for the spatial variability of hydraulic conductivity that is inherent to
the soil in natural slopes. Due to the high uncertainty of rainfall-induced failures in
natural slopes, many studies (Caine, 1980; Crozier and Eyles, 1980; Pradel and Raad,
1993; Fourie, 1996; Guzzetti et al., 2007) have attempted to predict landslide
probability, and therefore minimise the consequences. The theoretical background to
the above is reviewed in the following sections.
14
2.2 SLOPE FAILURE MECHANISMS
Slope failure mechanisms are governed by various controlling factors, and failure
occurs along the weakest paths in the slopes or more specifically the path where the
shear stress exceeds the shear strength. These paths may vary in their shape and
constitution, depending on the characteristics of the slope soil. These failure
mechanisms are briefly discussed below.
2.2.1 Characterising residual soil slopes in tropical regions
Residual soil is a term used to differentiate one type of soil from the more dominant
type, i.e. transported soil. Residual soil is created by the in-situ weathering and
decomposition of the soil’s original parent rock (Blight and Leong, 0 ) . Tropical
climates strongly influence the formation of residual soils (Morin and Ayetey, 1971;
Weinert, 1974), thus governing their characteristics. In tropical regions, the extremes
of alternation between intense rainfall and hot temperatures exert a rapid weathering
influence and cause leaching of the mobile constituents of the soil (Strakhov, 1967).
The combined effects of weathering and possible stress release from erosion can
expand and crack the weathered rock, producing small particles and clay minerals and
creating a system of interconnected voids. This condition makes residual soils both
more compressible and permeable to penetration by air and water than other types of
soil. As a result, residual soil slopes are susceptible to failure from prolonged heavy
rainfall, a typical scenario in the humid tropics. Unlike natural slopes, constructed soil
slopes such as embankments and reinforced soil slopes are designed to withstand
specified loads, usually include drainage, are compacted and essentially safer.
The groundwater table in residual soil slopes is often deep, located at depths of 5 m to
10 m below the slope surface (Blight and Leong, 2012), and is subject to fluctuations
from climatic effects (Wesley, 2010) as illustrated in Figure 2.1. In this situation, the
contribution of negative pore pressure or matric suction above the water table, in the
unsaturated soil zone, is significant to slope stability. The effects of unsaturated soils
should therefore be considered, along with slope stability, in geotechnical design.
There are many studies in relation to the effects of unsaturated conditions on soil
properties (Bishop and Blight, 1963; Blight, 1967; Fredlund and Morgestern, 1977;
Fredlund et al., 1978; Fredlund and Rahardjo, 1993). However, in the unsaturated
15
situations discussed here, the shear strength for saturated soils, which is usually
calculated utilising Equation 2.1 (below), (Terzaghi, 1950) does not apply.
Figure 2.1 Seasonal variation in water table and pore pressure due to climatic effects
(after Wesley, 2010)
τ = c′ + (σ – uw) tan ϕ′ (2.1)
where τ is shear strength, c′ is effective cohesion, σ is normal stress, uw is pore-water
pressure, and ϕ′ is the effective internal friction angle.
Unsaturated soils require additional parameters to calculate their shear strength, as
shown in Equation 2.2 (Fredlund and Rahardjo, 1993).
τ = c′ + (σ – ua) tan ϕ′ + (ua – uw) tan ϕb (2.2)
where (σ – ua) is net normal stress, ua is pore-air pressure, (ua – uw) is matric suction,
and ϕb is the angle indicating the rate of increase in shear strength relative to matric
suction.
The correlation between water content and matric suction provides significant data for
unsaturated soil characterisation. The curve illustrating this relationship is called a soil
water characteristic curve (SWCC), as shown in Figure 2.2. Errors or deviations in
laboratory tests may be accounted for by using the best-fit curve for the SWCC data,
16
as demonstrated in Equation 2.3 (below), (Fredlund and Xing, 1994). Significant
parameters associated with the SWCC are a, m and n, known as the SWCC
parameters. These parameters are usually used as inputs in geotechnical engineering
analysis, including slope stability analysis, when dealing with unsaturated soils.
Typical values of the parameters indicate types and characteristics of a soil, as
illustrated in Figure 2.2 (Fredlund and Xing, 1994). The SWCC indicates the water
storage capacity of a soil, and it can be used to determine the matric suction based on
the water content of the soil.
mn
s
ae
C
ln
)( (2.3)
where C(ψ) is a correction function, ranging from 1 for low suction to 0 for high
suction (ψ = 106 kPa), θs is the saturated volumetric water content, e is the natural
number (e = 2.71828), ψ is matric suction (kPa), and a, m, n are parameters
controlling the SWCC shape (indicating air-entry value, the shape near the air-entry
value, the slope of the SWCC and the residual water content, respectively).
Figure 2.2 Typical SWCC for different types of soil (after Fredlund and Xing, 1994)
17
The hydraulic conductivity of unsaturated soils varies with their degree of saturation.
The correlation between the degree of saturation, or water content, and unsaturated
hydraulic conductivity can be depicted using Equation 2.4, as proposed by Campbell
(1974), (illustrated in Figure 2.3). Soil types were indicated by f for fine-grained soils
with two soil parameters (a and ks) which directly affect the rainwater infiltration.
kw = (ks- kmin) (Θ)p + kmin (2.4)
where kw is the unsaturated hydraulic conductivity, ks is the saturated hydraulic
conductivity, kmin is the minimum hydraulic conductivity, Θ is the normalised
volumetric water content (= θw/θs), and p is the power factor for adjusting the
prediction (p = 4 is commonly used).
This correlation indicates that matric suction (ua – uw) is inversely proportional to
unsaturated hydraulic conductivity (kw). Hydraulic conductivity describes the ability
of a soil to transmit water through its voids.
Figure 2.3 Hydraulic conductivity function for unsaturated soils (after Rahardjo et al.,
2007)
18
Saturated hydraulic conductivity becomes a limiting value of the infiltration rate, as
illustrated in Figure 2.4. Initially, the infiltration rate in unsaturated soils is relatively
high, and then it decreases significantly as the degree of saturation increases up to the
lowest value in the saturated condition (Tholin and Kiefer, 1959).
Figure 2.4 Progress of infiltration through initially unsaturated soils during rainfall
(after Tholin and Kiefer, 1959)
Soils are naturally variable, due to the continuous and changeable nature of soil
formation. The inherent variation of soil properties from one point to another is not a
completely random process, rather it is spatially correlated, i.e., controlled by location
in space. The magnitude of soil properties at two close locations is likely to be
strongly correlated. This correlation weakens as the distance between the two
locations increases until no correlation can be made. Vanmarcke (1977a) suggested
that such spatial correlation should be considered in the modelling of soil properties.
It was revealed that the hydraulic properties of soil are the most variable, with its
coefficients of variation (CV) for hydraulic conductivity ranging from 27% to 767%
(Benson, 1993), while shear strength is the least variable, with a CV ≤ 45% obtained
from common in-situ tests (Kulhawy and Trautman, 1996).
0
1
2
3
4
5
6
0 0.5 1 1.5 2
Rat
io t
o in
filt
rati
on
cap
acit
y at
1 h
Time (h)
Saturated hydraulic conductivity
conductivity
19
It should be noted that tropical factors influence the characteristics of residual soil
slopes in relation to their vulnerability to shallow failure. Due to the deep