rock slope risk assessment

1. Original Paper
Landslides (2011) 8:221–231
DOI 10.1007/s10346-010-0246-4
Received: 16 March 2009
Accepted: 1 November 2010
Published online: 2 December 2010
© Springer-Verlag 2010
Anna M. Ferrero I Maria Migliazza I Riccardo Roncella I Elena Rabbi
Rock slopes risk assessment based on advanced
geostructural survey techniques
Abstract The rock mass structure determines the possible
unstable blocks that can induce rock fall phenomena. The stability
analyses must therefore be based on an accurate geo-structural
survey. In this work, the stability conditions of several steep
slopes along a motorway in the Far East have been evaluated
through key block analysis based on traditional surveys and on
laser scanner acquisitions. Discontinuity orientations and positions on the rock face are derived from the point cloud in order to
perform the reconstruction of the rock mass and to identify
blocks in the slope. Results obtained from both the traditional
and the new method is in good agreement. Stability analyses have
been performed for evaluating the kinematic feasibility of different failure mechanisms. The rock block shapes and volumes are
computed by performing 2D and 3D analyses whereas the failure
mechanisms are examined using the key block method. Parametrical analyses have been carried on to evaluate the influence
of slope angle variation. DEM models have also been set up. The
relative hazard is determined by statistically evaluating the
kinematical feasibility of different failure mechanisms. Hazard
mapping has been utilized to identify the best methodology for
risk mitigation.
Keywords Rock slopes . Risk assessment . Advanced
geostructural survey techniques
Introduction
In mountainous regions, transportation corridors are often
susceptible to landslides and, in particular, rock falls constitute
a major hazard in numerous rock cuts. This is the case illustrated
in this work, which concerns a highway segment in North
Malaysia (Fig. 1), about 5 km long, excavated through eight slopes
and affected by several rock falls that produce a possible risk for
highway users.
The aim of the study was to establish the prevailing rock
mass characteristics in the eight slopes for the evaluation of
instability phenomena based on a traditional geostructural survey
coupled with Light Detection and Ranging (LIDAR) technology, in
order to assess the relative hazard for the slopes, thus providing
recommendations for remedial works.
The analyzed rock slopes have been excavated by blasting
technique and are made up by very steep berms of 10 m heights
with global extension varying between 70 and 760 m in length and
30 and 135 m in height. Protection system and consolidation work
design needs the slope hazard evaluation in order to determine
optimal works and priority interventions.
Due to the large dimensions of the slope, traditional compass
surveys have been coupled with advanced techniques in order to obtain
geo structural information even without direct access to the rock mass.
A detailed 3D model of the rock slope topography (digital surface
model, DSM) has been acquired by laser scanning that allows the
acquisition of a very large number of measurements points forming a
“cloud of points”.
Acquired data have than been treated by applying the RANdom
Sample Consensus (RANSAC) algorithm (Fischler and Bolles 1981), that
allows the segmentation of the point cloud into subsets, each made of
points measured on a discontinuity plane of the rock face. For each
subset, the plane’s equation coefficients are first determined by robust
estimation and then refined by least-squares estimation after outlier
removal. The segmentation algorithm has been implemented in
software specifically developed, ROCKSCAN (Ferrero et al. 2008) to
facilitate the interaction with the point cloud in the identification of the
discontinuities by a virtual projection of the three-dimensional (3D)
data on a geo-referenced digital image of the slope.
In this way, selecting a rock mass portion directly on the
photographs by either a manual or an automatic system, the code
subdivides the area in point subsets belonging to single planes of
discontinuity. The code computes each equation orientation and other
relevant geometrical data of the plane.
Each slope has been scanned by two different laser scanning
surveys. The first one was performed on the entire slope surface in order
to determine the global slope DSM (Fig. 2a). A second survey with high
precision (Fig. 2b), was carried out on smaller slope portions (10×10 m
windows). The number of high precision windows for each slope was
proportional to the slope dimension and to the rock mass structural
features.
Data acquired with the two different approaches (compass and
LIDAR) have been merged together in a consistent data set and are then
statistically treated. This has led to recognition of typical discontinuities
for each slope describing them from a geomechanical point of view.
Once both the topography of the slope and the geo-structure were
determined, stability analyses were performed using the key block
method. The different possible kinematic modes (planar and wedge
sliding, toppling) were determined and factors of safety and volumes of
the possible unstable blocks calculated. An example of the stability
analysis based on a complete rock mass geometrical reconstruction is
also presented.
An index of stability has then been applied in order to assess a level
of relative hazard for the different slopes. This index has been defined
introducing parameters such as geometrical characteristics of the slopes
and of the berms, global stability and stability of the berms, presence of
water, and presence of protections. On this base remedial work,
typologies have also been suggested.
The words “joint”, “fracture”, and “discontinuity” are used in an
interchangeable way in the text.
The methodological approach followed for stability analyses
described in this paper is shown in Fig. 3.
Geostructural studies
Geological setting
The slopes are composed of porphyriticbiotite granite of Triassic
Age belonging to the Kledang Range. Quartz veins, aplite dykes,
and pegmatites of variable orientation and size are also present
within the granite rock.
Landslides 8 & (2011)
221

2. Original Paper
GPS
LIDAR
Compass on traverse
Georeferenced
Points
Clouds of
Georeferenced Points
Georeferenced
Digital Image
ROCKSCAN
3D Digital Terrain
Georeferenced Model
Geological and
Geostructural Survey
Statistical Analysis
Fig. 1 Profile of one (W4) of the eight slopes present along the highway
segment
A variety of structural discontinuity planes cut the granitic
rock; the discontinuity planes are of variable orientation, spacing
and extent and they produce rock blocks of variable size and
shape. The slopes belong to the same geological domain although
they show a different weathering degree: fresh to slightly
weathered granite rock is only exposed in the lower benches of
the selected slope cuts, the upper benches being excavated in
moderately to completely weathered rock.
Geostructural survey
Traditional methods
Geological-geostructural mapping was carried out for the eight
slopes through the definition of geostructural domains and the
geomechanical description of the rock mass (Ferrero et al. 2007).
For each slope, a preliminary geometric description was given
with the definition of geostructural domains and principal joint
sets (Fig. 4); then, a series of geostructural surveys along scanlines
of 10 m lengths were performed.
More than 50 geostructural traverses were performed with a
total of about 2,400 discontinuities collected in terms of
Joint sets definition
Editing
protection
measures
Rock Fall Hazard Maps
Propose of typological remedial works
Fig. 3 Methodological flow chart
orientation (dip, dip direction), spacing, persistence, roughness,
general condition (alteration, aperture, filling) according to ISRM
suggested method (1978).
In order to identify the predominant joint sets, all data
collected were statistically analyzed separately for each traverse
and together for the eight different slopes, using a commercial
code (DIPS, Rockscience). The combined use of these tools
permitted the determination of dispersion around the mean
value, in terms of a cone of confidence for each family of joints.
Fig. 2 Solid model obtained by laser scanning of the slope W4: a whole slope, b high precision survey on a berm window
222
Landslides 8 & (2011)
Stability Analysis

3. Fig. 4 Principal joint sets observed on one of the eight rock slopes (W4)
During the traditional survey, in situ observations of local
instabilities, water presence, and existing protective structures
were noted separately to be compared with and for integrating the
results of the surveys.
Laser scanner survey
The DSM generation for the eight slopes has been obtained by
using the LIDAR terrestrial laser scanner technique, which utilizes
a system consisting of a laser telemeter and a scanning mechanism.
A pulse emitted from the laser source is reflected by the object
surface, its echo is captured by the optics: measuring the time-offlight, the sensor-to-object distance is computed. Terrestrial lasers
are equipped with two mirrors mounted on two orthogonal axes;
when the instrument is leveled, the synchronized rotation provides
scanning in azimuth and zenith. The polar coordinates of the target
are then converted to a local Cartesian frame with theorigin in the
instrument center, z-axis vertical and x-axis in an arbitrary
direction. Point clouds of rock faces, (operating ranges of lasers
are from 100 to 800 m and more), with accuracies of the 3D
coordinates in the range 5 10−3 ÷3 10−2 m and a scanning role from
2,000 to 12,000 pts/s have been obtained. Angular scanning
resolutions are in the order of 100 mrad and allow for a very high
sampling density on the object in relatively short acquisition times,
resulting in millions of points measured on the object surface.
The survey of the slopes was carried out with a Riegl LMSZ420i with a calibrated Nikon D70 digital camera mounted on it.
During the survey, many scan positions were adopted in order to
avoid hidden zones. In addition, in each slope two different survey
resolutions were adopted:
for the general description of the slope, a point every 0.05×
0.05 m2 was acquired, while for taking the digital images a
20 mm calibrated focal lens was used;
a detailed survey was carried out in zones, having a dimension
of 10×10 m (a point every centimeter, 84 mm lens).
The examined slopes have been excavated by means of blasting
techniques (Fig. 4). Therefore, the free surfaces of discontinuities
present very little contrast and the survey requires high point density
and digital images having a very high resolution.
Fig. 5 Positions of surveyed planes, by using RockScan program, in one of the survey window
Landslides 8 & (2011)
223

4. Original Paper
rectangle base b = 1;
grid spacing = k*min(b,h)
rectangle height h
0.2
1
grid spacing (k=0.1)
# pts
grid spacing (k=0.2)
# pts
grid spacing (k=0.5)
# pts
grid spacing (k=1)
# pts
0.02
561
0.04
156
0.1
33
0.2
12
0.1
121
0.2
36
0.5
9
1
4
5
0.1
561
0.2
156
0.5
33
1
12
Topographical
survey direction
grammetry) has been analyzed with mathematical and stochastic
models to define it as a function of the most relevant
parameters: the accuracy on 3D coordinates of the points
surveyed on the discontinuity plane, the orientation, the size
and the shape of the plane respect to the direction of the
survey, and the number of points measured per unit area of
the surveyed discontinuity. In this approach, the discontinuity
plane (Fig. 6) is represented by a rectangular surface having
the base b fixed and the ratio b/h (where h is the rectangle
height) varying from 1/5 to 5 to represent elongated shapes in
height and width (as well as a square). The measurement
points are distributed on the rectangle on a square grid with a
point density k ranging in 10–100% (the percentage is referred
to the shortest rectangle side). The number of grid points
measured on each rectangle is a function of grid spacing and
the point density increases with the factor k and the rectangle
height decreases (table in Fig. 6). The accuracy of dip and dip
direction, as a function of measurement accuracy, has been
computed by variance propagation within a generalized least
squares model:
Dy ¼ Ax þ d
224
Landslides 8 & (2011)
a
1.4
12 pts
33 pts
156 pts
561 pts
1.2
Dip accuracy [deg]
A local network has been implemented, documented, and
surveyed by means of a fast static GPS survey with a Trimble
4,000 ssi double-frequency receiver and a Trimble 4,600 singlefrequency receiver, in order to provide reference points to
georeference the scanning.
From these points, a topographic survey was carried out by
means of a Leica TC 1,105 total station to connect the reflecting
targets placed on the rock slope to the reference vertices. In this way,
it was possible to convert all the local measurements into a mapping
system and reference all data to the north direction.
The laser scanner supplies the coordinates of points in space.
The next step to realize a geometrical model of the rock mass is the
determination of the discontinuity planes. For this purpose, points
have to be divided into groups belonging to a single plane. In other
words, the point cloud has to be analyzed in order to identify the
points belonging to each discontinuity plane existing in the slope.
For this purpose, laser scanner measurements have been superimposed onto images of the slope in order to determine both slope
geometry and identify rock discontinuities by means of a software
called ROCKSCAN developed by the authors (Fig. 5). This tool is
based on a segmentation algorithm capable of identifying the
number of planes present in a point cloud and compute their
geometrical parameters. By knowing the plane equation, dip and dip
direction of each plane can be computed. A detailed description of
the applied survey technique is given in Ferrero et al. (2008) where a
description of the software ROCKSCAN developed by the authors is
also given.
The accuracy (i.e., the degree of closeness of measurements of a
quantity to its true value) of the dip and dip direction estimation by
mean of non contact method of survey (laser scanning or photo-
1
0.8
0.6
0.4
0.2
0
10
b
Dip Direction accuracy [deg]
Fig. 6 Geometrical discontinuity plane characteristic and point density value
considered to define the accuracy of the orientation plane definition
with parameters x and observables y (Felus 2006).
Without loss of generality, the equation of a plane through
the origin ax þ by þ cz ¼ 0 has been considered; the functional
model is of the form F(y, x)=0 and must be linearized with
20
30
40
50
60
DIP [deg]
70
80
90
7
12 pts
33 pts
156 pts
561 pts
6
5
4
3
2
1
0
10
20
30
40
50
60
70
80
90
DIP [deg]
Fig. 7 Dip (a) and dip direction (b) accuracy computation for a rectangular shape
(h=5 and b=1) of discontinuity plane for different value of plane inclination (dip)
and point density

5. respect to the observables as well as with respect to the
parameters. We have therefore:
D ¼ @F ;
@y
A¼À
@F
;
@x
d ¼ ÀF ðxo ; yo Þ
where D contains the parameters of the plane and A the
coordinates of the points which define the plane, while xo, yo
are respectively approximations of parameters and point coordinates. The stochastic model is defined by the covariance matrix
of the observations CYY, which is taken as block diagonal,
neglecting correlations between measurement points.
The theoretical accuracy of the parameters is given by the
covariance matrix CXX computed by covariance propagation:
À
À1 ÁÀ1
CXX ¼ At ðD CYY Dt Þ A
The orientation of the plane unit normal vector (pole)
pointing upwards can be expressed as a function of the plane’s
coefficient as
dip ¼ arccosðcÞ dip direction ¼ k Æ arctanða; bÞ
where k is 0° or 180° depending on the quadrant. The accuracy of
dip and dip direction determination can be derived by a new
error propagation, with the full covariance matrix CXX.
The analyses carried out have been shown as the accuracy of
dip depends only on the accuracy of the z component of the
vector normal to the plane, while the accuracy of dip direction
depends on the accuracy of both the x and y components (but x
and y components accuracies are strongly influenced by the z
component itself). Consequently, the accuracy in estimating the
plane dip direction is strongly influenced by the plane dip angle
for nearly horizontal planes (below 30°) as well. To evaluate the
accuracy value obtainable for both dip and dip direction, several
simulations have been performed, using different shapes and
size plane and density of points on the face plane. The results
show as the accuracy always increases with a higher point
density. The dip accuracy, it strongly depends on the shape of
the plane and less from the dip value of the plane; while, the dip
direction accuracy depends on both shape and dip value of the
plane. In Fig. 7, an example of the result obtained is illustrated.
In this case, the results regard a rectangular shape of the plane
and the dip and dip direction accuracies are referred to
different values of plane inclination (dip) and number of points
in the plane.
One can observe as the error in the estimation of the plane
orientation decreases with plane dip. It is necessary to note that
the values of mean square error have been obtained by
considering an horizontal survey direction; the relation between
the measure error and the dip value is correlated to the
direction of the survey in relation to the slope direction (for
instance if the survey is vertical the error is higher for vertical
planes and minimal for horizontal planes).
Graphs similar to those reported in Fig. 7 have been
developed for planes with different shapes and orientations for
the design of a laser scanning survey with a known accuracy. In
this way, the survey orientation with respect to the rock slope
and the point density to be measured can be defined.
For what it concerns persistence, spacing and discontinuity
position in the space, the code ROCKSCAN allows to determine
all geometrical characteristics of each identified plane and trace.
In particular, persistence can be computed by the code by
selecting two opposite extreme points on the rock face.
Spacing can be defined in two ways: the first one simulates
the classical compass survey along scanline by reproducing a
virtual scanline on the photographs and counting the distance
of each intersected plane by an interactive tool; the second way
(a)
Major
joint sets
J1
J2
J3
Slope
(b)
Compass survey
Dip
Dip Direction
76
242
72
006
37
192
88
200
Dip
77
88
44
88
LIDAR survey
Dip Direction
241
195
224
200
Fig. 8 Joint set identification determined by the compass survey (a) and by laser scanner DTM (b) on the same zone of the slope: slope W4–traverse 3 (lower
hemisphere)
Landslides 8 & (2011)
225

6. Original Paper
is to select two discontinuities between which the code
computes the minimum distance in mathematical way. The
plane localization is done automatically by the code knowing
the 3D coordinate of the plane centroid.
Table 1 Joint sets orientation angles and average spacing value obtained by
statistical analysis of the data collected along each slope by traditional and LIDAR
surveys
Stability assessment
Fractured rock masses are often geometrically complex and can
be regarded as an assemblage of many individual polyhedral
blocks whose shape and volume are connected to number,
orientation, and spacing of the discontinuity systems present in
the rock mass. When such a rock mass is subjected to
mechanical disturbance, through for example the excavation
of slopes, the rock blocks can displace, rotate, and detach from
the rock mass.
To assess the slopes stability conditions, several analyses
have then been performed by applying the limit equilibrium
method (LEM). Several analyses were performed by considering the statistical distribution of geometrical characteristics of
the joint sets identified in each slope.
In order to identify shape, dimension, type of kinematism
and factor of safety of the blocks that can detach from the rock
mass, the commercial code Rock3D (geo&soft) has been utilized.
The code allows to conduct slope stability analyses following
four steps: cluster analysis to identify the joints sets by
thehierarchic clustering procedure; kinematic analysis based on
the key block theory (Goodman and Shi 1985); geometrical
reconstruction of the blocks by creating a map of the discontinuities on the rock face, based on the statistical distribution of
the discontinuities measured on the slope; stability analysis by
applying the limit equilibrium method to compute the factor of
226
Landslides 8 & (2011)
Joint
Sets
Dip
[°]
Dip Dir
[°]
1 (311 data)
J1
80
309
0.5
J2
89
174
0.6
J3
72
75
0.8
J4
53
146
0.5
J5
48
83
0.7
J1
87
42
0.7
J2
81
341
0.7
J3
51
160
0.45
J4
54
232
1.5
J1
33
285
1.2
J2
77
329
0.6
J3
71
248
0.5
J4
84
63
0.6
J1
69
10
0.6
J2
82
239
1
J3
35
212
0.65
J4
62
136
0.7
J1
79
9
0.4
J2
77
239
1.2
J3
83
132
0.8
J4
48
191
0.6
J5
Geo-structural data analysis and comparison
Orientation data calculated from LIDAR and those measured
through compass have been compared to validate the system. In
Fig. 8, an example of the comparison of the two stereonets
obtained plotting the data resulting from the traditional compass
method and from LIDAR data is reported. The results refer to the
data collected along a traverse (37 data plane collected) and in a
LIDAR survey windows placed in the same zone (251 data plane
collected). The results appear in good correspondence with the
preliminary in situ observations apart from the sub-horizontal
plane that cannot be detected by laser scanner since all
acquisition have been done at the same high.
Data have been analyzed after subdividing the slope into
homogeneous domains, and discontinuity data have been statistically analyzed to define the joint sets and their average
orientation, spacing, and persistence (Table 1). The rock mass
has shown a relatively homogeneous structure in that the main
joint sets are present in all slopes although some of the slopes
have shown a local variation. In particular, in some slopes a joint
set parallel to the rock face has been observed by the in situ
survey that cannot be identified from the LIDAR data.
Discontinuity spacing and persistence distributions have
been computed and average values of spacing are utilized in the
rock slope stability evaluation. Concerning persistence, the
computed values have been high for most joint sets (above
90%) with very high dispersions and, consequently, several
values of persistence have been assumed, with a maximum value
of 95%.
Slope
26
207
1.0
J1
70
264
0.5
J2
80
12
0.7
J3
49
168
1
J4
79
134
0.7
J5
76
302
1
2 (1,100 data)
3 (338data)
4 (376 data)
5 (419 data)
6 (400 data)
7 (467 data)
Spacing
[m]
82
212
0.3
50
187
0.6
J3
41
288
0.7
J4
45
40
0.5
J1
77
63
0.4
J2
75
215
0.43
J3
44
215
0.5
J4
70
268
0.64
J5
8 (515 data)
J1
J2
78
11
0.5
safety of each finite and removable block and, in case of unstable
blocks, the stabilization forces.
Cluster analyses leads to the identification of the joints sets
by hierarchic clustering procedures based on multivariate

7. 0111
4
0011
0110
0101
0001
1001
Kinematism
1
0100
Planar sliding
0000 1100
1000
slope
Wedge sliding
Joint /
Intersectio
n
Block
ID
J3
J3-J4
J2-J4
10001
10011
10101
Max
Block
Volume
[m3]
5.200
0.008
0.134
Safety
Factor
SF
0.937
0.900
0.510
1011
3
1110
2
1010
Fig. 9 Joint pyramids obtained for slope W4 and relative types of kinematics,
maximum volume of the free and removable blocks and corresponding safety
factor. In the lower side of the figure, a statistical reconstruction trace map and
free and removable blocks identified for three-dimensional sliding along the
intersection J3–J4 (red and blue lines in Fig. 4) are reported
analysis applied to the bi-dimensional spherical space instead of
the n-dimensional Cartesian space (Dillon and Goldstein 1984).
This procedure leads to the determination of an optimal number
of joint sets and their average orientations. The stability analysis
are carried out by identifying the possible kinematic mechanism
(vertical fall, planar, and wedge sliding) with the key block
method, evaluating the rock block volume and determining its
safety factor (SF) on the base of the shear strength of the
discontinuities and by applying the LEM.
Joint shear resistance has been determined on the basis of
discontinuity roughness and compressive strength collected by in
situ measurements. In particular, since all discontinuities constantly
showed low roughness values and a high weathering degree a
precautionary friction value equal to 32° have been adopted for all
stability analysis.
This code allows, note the rock face dimension and orientation,
to determine a map of the discontinuity traces in two different ways:
by introducing the orientation and position of each discontinuity on
the rock face (deterministic way) or by an automatically traces
generation based on statistical distribution of geometrical discontinuity characteristics (orientation, spacing, and length) measured on
the slope (random way). In this way, the survey results can be
expanded and applied to larger slope portions. Statistical analyses of
spacing and persistence are carried independently for each joint set
identified by the cluster analysis. For each kinematic analysis, the
block shape and size is defined by the joint intersection, spacing, and
persistence; so they can be characterized by simple or complex shape
and the volume of each detachable block (defined as free and
removable in the key block method) can be easily determined by
simple analytical geometric equations. The dip, dip direction, and
spacing variation for each joint set was quantified and applied in the
block stability evaluation by considering a random combination of
this variability. Several analyses for each block type were performed
until the maximum volume for each kinematics type was determined.
Stereographic projection analyses have been performed for each
slope both considering average slope dip and berm dip. These
analyses allow to identify toppling of free and removable blocks and
Table 2 Geometrical characteristics of the analyzed slopes and results obtained in terms of number of possible kinematisms, number, and volume of detachable (free
and removable) blocks
Slope
Height
Length
Dip
[m]
[m]
[°]
Number of
Kinematism
#
Free and
Removable Blocks
#
Max Block Volume
3
Average Block Volume
[m ]
[m3]
W1
40
180
60
4
23
1,118
0,291
W2
135
750
75
6
59
23,0
1,830
W3
30
100
75
3
15
0,556
0,107
W4
30
90
75
3
9
5,217
0,614
W5
40
50
80
6
31
5,430
0,896
W6
50
130
82
3
28
3,411
0,336
W7
50
250
85
3
38
4,342
0,588
W8
50
50
80
4
22
0,983
0,134
Landslides 8 & (2011)
227

8. Original Paper
to define their SF on the base of the block geometrical conditions
(ratio between block height and width) and the base plane dip.
This method has been applied to each analyzed slope, considering all the acquired discontinuity data obtained from traditional
and laser scanner surveys. The results of the key block analysis are: a
map of the discontinuities crossing the rock faces, the type of
kinematics, and the geometrical features of free and removable blocks.
For each free and removable block, the SF was computed by the limit
equilibrium method. Results are reported in Fig. 9 for slope W8.
In Table 2, the results obtained for all the eight slopes analyzed
are summarized.
A parametrical analysis has been performed in order to evaluate
the influence of the slope orientation on the slope stability.
The slopes considered with average inclination were analyzed
with special care since it gives an important indication on the whole
slope stability conditions. In particular, a decreasing dip can
determine a decreasing number of possible kinematism types since
it determines a decreasing of the “space pyramid”.
Average and standard deviation dip for each slope have been
computed analytically by the developed code as the plane that
interpolates the whole cloud points. Computed dip range has been
utilized for performing parametric analysis.
Some of the slopes did not show any major variation (slope W1,
W2 zones 1 and 3, W3, W6, and W7) in the stability condition because
the decreasing dip did not determine any variation in the block
pyramid in terms of finite and removable blocks.
Some slopes (W2 zone 2, W4, W5, and W8) show variations
indicated in Table 2.
In practice, some of kinematism are not present any more with
these slope inclination decrease. As smaller number of blocks reduces
the number of unstable blocks and, consequently, the unstable volume
per unit slope area. However, the maximum volume block did not
change with slope inclination since in all cases the kinematism that
determined that maximum volume is constantly the same.
In certain cases, if the slope inclination increases of few degrees
(and this can happen locally for large size slopes) the kinematism can
be formed by the discontinuity intersection. For this reason, a
parametrical analysis has been performed in order to evaluate when
a dip variation can determine major slope stability variations too (dip
max in Table 3).
This is the case of slope W2 zone 2 where an increase of 8° (up
to 67°) can determine the formation of blocks of large dimensions
(up to 20 m3). For slopes W5 and W8 the slope dip variations (down
to 40° and 48°, respectively) determine an increase in the slope
stability conditions.
In Fig. 10, some pictures reporting the in situ sliding phenomenon
are shown, these observations confirm the results obtained with key
block methods and have been carried on for each analyzed slope.
Stability analyses based on the complete geometrical model of the
slopes based on the above described survey have been done by
applying the code Resoblock (Héliot 1988a, 1988b). The analyses follow
the tectonic history of the formation as a continuous medium is
Table 3 Parametric analysis results by varying the slope dip between dip value obtained by laser scanner results DTM, up to the minimum dip determining unstable
blocks
Slope
Laser scanner
Dip
Dip direction
W1
57
164
W2
59
161
Parametric analysis
Dip “max”
Notes
Observed variation in the stability conditions with slope dip
No changes
67
Zone 1–3 (DD 155°). No changes
Zone 2 (DD 170°).
With dip equal 59° kinematism 1,110 (3D sliding along J1–J4) is
not more present. This kinematism is present from a slope
minimum dip equal 67°
W3
65
255
W4
54
196
No changes
65
Kinematism 1,010 (sliding along J2–J4) and 1,001 (sliding along J3–J4)
are not more present with dip equal 54°
This kinematism is present from a slope minimum dip equal 65°.
Kinematism with max volume (>5 m3) is always present
W5
40
190
62–51
Zone 1 (DD 220°). With dip equal 40°Kinematism 10,101 (sliding along J3–J4),
10,001 (sliding along J4) and 10,011 (sliding along J2–J3). Kinematism with
max volumes (10,101: > 3.5 m3 e 10,001>2 m3) are present from a
minimum slope dip equal 62°
Zone 2 (DD 180°) With dip equal 40° kinematism 11,001 (sliding along J2–J4),
10,001 (sliding along J4) and 10,011 (sliding along J2–J3). Among these
kinematism with max vol. (10,001>7 m3) is present from minimum
slope dip equal 51°
W6
178
No changes
W7
64
198
No changes
W8
228
71
49
207
Landslides 8 & (2011)
52–71
Kinematism 10,001 (planar sliding along J3) e 00,111 (sliding along J1–J2)
are not more present with slope dip equal 49°. Minimum dip equal for
one sliding 52° and 71° for both

9. Planar sliding
a)
b)
W2. along set J3(160˚/51).
d)
W4. along set J3 (212/35)
W2. intersection system J3 – J4.
3D sliding
c)
W4. intersection J2 – J4.
Fig. 10 Some pictures reporting the in situ planar (a and b) and 3D (c and d) sliding phenomena
transformed into a block system. The joint sets have been introduced
in the code in such a way that only the first one can determine planar
and continuous discontinuity while the following joint set have to stop
against existing planes. Discontinuities can be introduced in a
deterministic way, as in the case of faults or singular discontinuities
directly detected on site; the joint sets are automatically generated in a
statistical way on the basis of surveyed discontinuities, by means of
statistical distributions.
Figure 11 shows the Resoblock model (1,265 blocks reproducing a
rock mass volume of 100×40×40 m3 (X, Y, Z)) set up on this basis for
one slope and the sliding phenomena computed by the block stability
analysis code based on the limit equilibrium method. These analyses
have determined the same kind of kinematics obtained with the
Rock3D code in all examined cases.
Hazard assessment methodology and results
In order to plan and define the eventual remedial works needed to
reduce the rockfall risk level, a hazard level assessment procedure
based on the use of intensity–frequency matrix diagram (OFAT,
OFEE, OFEFP 1997; Interreg 2001; Costa et al. 2006; Corominas et
Fig. 11 Resoblock rock slope reconstruction of slope W2 and sliding blocks computed by BSA code
Landslides 8 & (2011)
229

10. Original Paper
DANGER
HAZARD LEVEL and RATING
High to
very high
Low to medium
1÷5
High to Very High
Medium
to high
0÷1
Medium to High
>5
Low to
medium
High to Medium
very high to high
Low to
medium
PROBABILITY OF ROCK FAILURE
Fig. 12 Danger–probability of rock failure diagram to obtain the hazard level
(from OFAT, OFEE, OFEFP 1997 modified)
al. 2003, Jaboyedoff et al. 2005) has been applied. For this purpose,
the hazard is calculated in term of probability of occurrence of a
dangerous phenomenon in a given location and time period
(Varnes and IAEG Commission on Landslides 1984). Consequently,
hazard level derives from a cross analysis between probability of
rock failure and danger, as reported in the diagram of Fig. 12.
The eight slopes have been preliminary analyzed focusing on the
characterization of danger (intensity or magnitude of a localized
existing or potential phenomenon of slope instability, with specific
geometric and mechanical characteristics). The danger has been correlated to the number and the volume of the unstable blocks. The rock
block volume has been evaluated by considering the discontinuity
orientations, persistence and spacing of each characteristic joint set.
The probability of rock failure is calculated for a portion of rock
mass, with a specific volume within the considered slope. For this
study, this item has been correlated to the SF coming from the analysis
developed on the slopes for the theoretical unstable blocks and to the
type and number of possible instability phenomena, as focused on the
basis of the kinematic analysis.
Table 4 Partial and total hazard rating obtained for each slope
Slope
Domain
Global stability [m3/m2]
Berm stability
Water
Existing protection
Total rating
0.7
1
1
−1
1.7
a
0.1
1.3
1
−1
1.4
b
3.5
10
1
−1
13.5
c
0.4
0
1
0
1.4
W3
0.3
0
0
0
0.3
W4
0.1
1
0
0
1.1
a
0.9
2
2
0
4.9
b
0.4
2
2
0
4.4
W6
4.4
1
2
0
7.4
W7
0.1
0.3
2
0
2.4
W8
0.1
0.2
0
0
0.3
W1
W2
W5
The gray hues are the same of the hazard levels reported in Fig. 12
Table 5 Typological proposed remedial works for the different slopes
Remedial work
SLOPES
3
8
4
2a
2c
1
7
5b
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Bolting
X
X
X
X
X
X
Mesh
X
X
X
X
X
X
X
X
Monitoring
Scaling
Fences
Re-profiling
Canopy
230
Landslides 8 & (2011)
6
2b
X
Real time monitoring
5a
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X

11. The risk is defined as the product between the slope hazard and
the vulnerability. Since the slopes are all directly hanging on the
motorway, they all show the same degree of vulnerability (100%) and,
consequently, the risk zonation corresponds to the hazard zonation.
In order to assess the exposition to the risk associated with rock
fall and to prioritize interventions, a classification scheme was
developed, to identify, the most dangerous slopes among the eight
slopes studied and those of them requiring more urgent remedial
works. Since all slopes are hanging on the motorway, the level of
vulnerability is constantly high for all slopes and consequently the
level of hazard can be directly compared. The very small space
between the slope and the road corridor do not leave any other
possibility.
To define the level of danger, and then the hazard level, the
following items and ratings have been taken in account:
– Geometrical characteristics of the berm (height, gradient,
length of the road below the slopes), considered as part of
global stability and stability of single berm;
– Global stability of the slope expressed in terms of unstable
theoretical volumes per slope square meter (unit volume),
varying from 0 to 10 m3/m2;
– Stability of the single berm expressed as unstable theoretical
volumes per square meter of slope, on slope height (varies
from 0 to 4.4);
– Presence of water: dry (0), damp (1), or seepage (2) are
distinguished;
– Presence of existing protection measures, ranging from 0
(absence or presence of non consistent protections) to −1
(existing protections).
According to the above mentioned index, a final rating was
assigned to each slope (or geo-structural homogeneous subdomain), as reported in Table 4. Taking in account the total rate,
three levels of hazard were distinguished (Fig. 12) and applied for
the various slopes and domains.
The above defined hazard levels allowed for setting up a “Map
of Rock Fall Hazard”. The maps were set up by subdividing each
slope in regular mesh of 30×30 m; for each mesh cell, n hazard level
was assigned on the bases of the above reported parameters.
According to this, some preliminary typological remedial works
have been proposed, for different levels of hazard, as shown in Table 5.
Conclusion
The paper aims to demonstrate the importance of advanced
techniques in the slope geo-structural and geometrical survey to
improve the quality of the stability analysis.
The study of the stability conditions of eight rock slopes
hanging on a motorway in Far East have been carried on by
means of the key block method based on accurate rock mass
surveys. The surveys have been performed by both classical
techniques and laser scanning acquisition; the last one has
allowed to determine the DSM and the slope point clouds that
have then been treated by a specific software developed by the
University of Parma for the determination of the rock discontinuities
visible on the rock faces by means of the application of a segmentation
algorithm. Discontinuity dip, dip direction, and position have also
been computed. Statistical data analysis at different scales supported
by in situ observation allowed the determination of the rock mass
structures in terms of joint set orientation and spacing
Consequently, finite and removable rock blocks have been
determined in terms of kinematics mode and maximum and average
unstable volumes. The acquired data will then be utilized for the
design of stabilization works and for the slope risk assessment.
On the basis of stability computations and in situ observations,
a stability index has been defined for both the global slope and
singular berm. Using this index coupled with geometrical characteristics of the slopes, derived from DSM file, a rating system has been
adopted for the hazard zonation. This procedure has also allowed to
set up a quantitative way to compare the hazard among the slopes
thus suggesting typological remedial and hazard mitigation works,
for the various kinematisms.
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A. M. Ferrero ()) : M. Migliazza : R. Roncella
Department of Civil Engineering, of the Environment, of the Territory and Architecture,
University of Parma,
Parma, Italy
e-mail: annamaria.ferrero@unipr.it
E. Rabbi
Geodata,
Turin, Italy
Landslides 8 & (2011)
231

12. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.