1. LANDSLIDE RISK REDUCTION BY COUPLING
MONITORING AND NUMERICAL MODELING
BOZZANO F.1, CIPRIANI I.1, ESPOSITO C.1, MARTINO S.1, MAZZANTI P.1, 2,
,PRESTININZI A.1, ROCCA A.1 & SCARASCIA MUGNOZZA G.1
Dipartimento di Scienze della Terra e Centro di Ricerca CERI– Sapienza Università di
Roma, P.le A. Moro 5 00185, Rome, Italy
2
NHAZCA S.r.l., spin-off “Sapienza” Università di Roma, Via Cori snc, 00177, Rome,
Italy
1
Landslide risk reduction by coupling
monitoring and numerical modelling

2. Outline
• The case history; short summary of slope-infrastructure
interaction
• Description of the methodological approach
• Geological and geomorphological background
• Field activities and preliminary geological model
• Monitoring activities: criteria of the monitoring platform
design; highlights on the main results
• Construction of the geomechanical model and numerical
back-analysis
• Some considerations
Landslide risk reduction by coupling
monitoring and numerical modelling

3. The case history
Frame: modernization of a major motorway in southern Italy
Study slope: construction of a new tunnel
Landslide risk reduction by coupling
monitoring and numerical modelling

4. The first landslide
March 2007
February 2007
Landslide risk reduction by coupling
monitoring and numerical modelling

5. Following this event, the Research Centre for Geological Risks CERI of the
University of Rome “Sapienza” carried out detailed engineering-geological
investigation and surveys (field geomorphological, geological and
geomechanical surveys, boreholes, seismic surveys and laboratory tests of
samples) on the slope in order to define a reference model to explain the
occurrence of the landslide and to plan the remediation works
Landslide risk reduction by coupling
monitoring and numerical modelling

6. Methodological approach
Geology – Structure – Gemorphology
(site surveys; field investigations)
PRELIMINARY GEOLOGICAL MODEL
OF THE SLOPE/LANDSLIDE
ue
ngr
Co
hec
yc
nc
k
Geomechanical data
MONITORING DATA (in depth
and surficial; geotechnical,
topographic, meteorological)
EMERGENCY
PHASES
MANAGEMENT
Strain (and stress)
history of the
slope
Calib
rat
rheol ion of
param ogical
eters
analy (backsis)
Back-analysis
Testing suitability of
semi-empirical models
for time of failure
forecasting
Landslide risk reduction by coupling
monitoring and numerical modelling
REFERENCE
GEOMECHANICAL
MODEL
NUMERICAL BACKANALYSIS OF SLOPEINFRASTRUCTURE
INTERACTION
INTEGRATED TOOL
FOR DISPLACEMENT
FORECASTING

7. Geological and geomorphological background
High Quaternary uplift rates: marine
terraces and steep slopes
Metamorphic bedrock covered by
marine/continental deposits
Landslide risk reduction by coupling
monitoring and numerical modelling

8. Geological model of the slope and kinematic model of
the landslide
• Site surveys (geological-
Marine terrace deposit
Sands
Landslide-involved
gneiss
•
Gneiss
•
March 2007 event: partial re-activation
of an existing complex, deep-seated,
roto-translational landslide
Landslide risk reduction by coupling
monitoring and numerical modelling
structural and
geomorphologic);
Stratigraphic logs from
boreholes;
Geophysical site
investigations

9. Monitoring activities
Before and during
the construction of
stabilization
countermeasures
1. Inclinometers
2. Piezometers
3. Terrestrial InSAR
4. Total station
5. Load cells on man-made
reinforcements
Landslide risk reduction by coupling
monitoring and numerical modelling
After the
construction of
stabilization
countermeasures
During the restart of
tunnel excavation

10. Layout of monitoring instrumentation
Landslide risk reduction by coupling
monitoring and numerical modelling

11. In-depth monitoring: inclinometers and piezometers
SAbis
SA
Sc
Inclinometer
Piezometer
Landslide risk reduction by coupling
monitoring and numerical modelling

12. Surface monitoring: TInSAR
Landslide risk reduction by coupling
monitoring and numerical modelling

13. Cognitive monitoring: slope movements before and during
the construction of stabilization countermeasures (slope reprofiling and retaining structures)
SAbis
SA
SB
Inclinometer
Piezometer
Landslide risk reduction by coupling
monitoring and numerical modelling

14. Cognitive monitoring: slope movements before and during
the construction of stabilization countermeasures (slope reprofiling and retaining structures)
Landslide risk reduction by coupling
monitoring and numerical modelling
t ne m c a psi D So L
e l
) mm
(
10 November 2007 – 29 February 2008

15. Control monitoring: displacements of the first remedial
works - gabions
Landslide risk reduction by coupling
monitoring and numerical modelling

16. Control monitoring: displacements of the first remedial
works - gabions
Landslide risk reduction by coupling monitoring
and numerical modelling

17. Control monitoring: displacements of the first remedial
works - bulkheads
Landslide risk reduction by coupling
monitoring and numerical modelling

18. Control monitoring: displacements
of the first remedial works bulkheads
Landslide risk reduction by coupling
monitoring and numerical modelling

19. Control monitoring: displacements related the
restarting of tunnel excavation
Bulkheads
Landslide risk reduction by coupling
monitoring and numerical modelling

20. Excavation stopped
Preparatory
works
Tunneling excavation progress
Lack of
TInSAR
data
Landslide risk reduction by coupling
monitoring and numerical modelling

21. Control monitoring: displacements related to the
restarting of tunnel excavation
Before the beginning of the tunnel excavation the anchored bulkheads showed an almost
constant velocity of displacement on the order of 0.05 mm/h. Immediately after the
beginning of the excavation the velocity of bulkheads suddenly increased reaching maximum
values of 0.75 mm/h with acceleration and deceleration peaks on the order of 0.02 mm/h2.
During the three excavation phases a maximum displacement of about 100 mm was
recorded on the first order of bulkhead. In the last two phases, the interferometric monitoring
allowed us to clearly recognize a typical creep behaviour.
Landslide risk reduction by coupling
monitoring and numerical modelling

22. Control monitoring: displacements related to the
restarting of tunnel excavation
Velocity of displacement: about 1 mm/hour.
Activation of a protocol to immediately stop tunneling
Landslide risk reduction by coupling
monitoring and numerical modelling

23. Some hints from the large collected dataset
•
•
•
During more than 40 months monitoring,
several shallow landslides were detected by
TInSAR images of the slope. In particular, ten
events were identified by TInSAR data and then
confirmed by optical photos
The large dataset of events occurred on the
same slope (which means similar conditions
and features of the landslides) and the
detailed displacement data available
represented an occasion to test the efficacy
of semi-empirical approaches based on time
series of displacement or derived quantities
(i.e velocity, acceleration etc).
The displacement behaviour of the 10 shallow
landslides, and especially in their pre-failure
stage, were analysed in detail in order to infer
information about the total amount of
displacement, the duration of the entire
process, the velocity, the acceleration, etc.
Landslide risk reduction by coupling
monitoring and numerical modelling

24. Some hints from the large collected dataset: testing
the suitability of semi-empirical models for time of
failure prediction
Creep behavior, except for
a slight deceleration
immediateley before failure
Landslide risk reduction by coupling
monitoring and numerical modelling

25. Test # 1: Fukozono (1985) linear model and its
modifications applied to shallow landslides
•
•
For each landslide, the predicted time of failure was computed iteratively since the
beginning of the displacement phase (looking at the tertiary creep phase) by
increasing the number of displacement data step by step. Hence, the real prediction
of the time of failure based on the newly collected data over time was simulated.
A new approach named ADF (Average Data Fukuzono) was developed. ADF is based on
the average and moving average velocity computed from temporal consecutive
data. In the first case, data were averaged iteratively, starting from the first data
collected. In the case of the moving average, the data were averaged by using the half of
the dataset moved iteratively by one single step until the last half before the failure.
Landslide risk reduction by coupling
monitoring and numerical modelling

26. Test # 2: non-linear approach for anchored bulkheads.
Landslide risk reduction by coupling
monitoring and numerical modelling

27. From the geological model to the geomechanical
model
Landslide risk reduction by coupling
monitoring and numerical modelling

28. Geomechanical surveys
Correlation Jv - Ib
25
20
Ib (cm) = -6,09 ln(Jv) + 30,06
R2 = 0,92
15
)
m
c
(
b
I
Ib (cm) = -5,76 ln(Jv) + 27,48
R2 = 0,90
10
Ib (cm)= -5,43 ln(Jv) + 24,90
R2 = 0,87
5
Data from 144 survey sites
0
0
5
10
15
20
Jv (n° discontinuità/m3)
Landslide risk reduction by coupling
monitoring and numerical modelling
25
30
35

29. AN ALTERNATIVE APPROACH FOR ROCK MASS CLASSIFICATION
Based on Jv and Ib values
1) z_score transformation
4
D30
D26
3
D4
D29
D14
D44
2
D42
Z40
Z3
GM6
S17
Z2
1
I
_
z b
D16
0
D19
ST2
Z6
ST1
D11GM8 D35
D33
ST6
Z28
ST25
D1
S3
Z1
ST3 GM1
S21
D3
D13 0,44
Z30
Z29 D8 D43
S19
S4
S14
S18
S9
D6
S27
D41
D9D21
S20
ST4
D2
ST9
STZ2 GM15
Z9
ST10
Z33
D37
Z8 GM7
ST8 D38
Z38
S8
ST5
D32
S5
Z5
Z4S2
D45
GM4
-1
Stimato
0,61
D27
D46 D36
S26
S15
Z36
S10
Z24
D15 Z39
S11
S29
Z27
GM5 D7
GM10
GM14
D34
0,06
-0,68
D40
GM9
Empirico
1,24
D28
D25
D23
1,88
1,77
GM11
0,03
D5
GM13
Z7 S22
D10
ST7
Z23
Z25
STZ1
S24Z20
S28
S7
GM3
GM12
D22
D12
D18
-0,50
GM16
-0,75
Z31
S25
Z22S23
S1 S13
S6
D39 D24
S12
Z21
Z35
Z37
S16
D20
-2
Z32
Z34
-1,66
GM2
D17
D31
-3
-3
-2
-1
0
1
z_Jv
Landslide risk reduction by coupling
monitoring and numerical modelling
2
3
4

30. AN ALTERNATIVE APPROACH FOR ROCK MASS CLASSIFICATION
2) hierarchical clustering (identification of rock mass classes)
4
GM3
GM7
3
D18
GM8
D4
1
D44
2
Z39
Optimal number of clusters (15)
based on the index by Calinski –
Harabasz (1974)
I
_
z b
S15
0
-1
11
12
D31
S3
S9 D37
D2
1
10
ST10
ST2
D39
GM5
GM4
GM10
ST5
13
14
S6
Z16
D43
S16 Z40
S2
D5
ST1
D10
Z5
D3 S1
ST8
S4
D29
D41 S14
S20
Z37
S29
D9
ST4
D40
S28
S12 D12Z29
S5
D36
Z36
D46
GM16
Z38
D1
S18
D11
Z2
GM2
Z33
D22
Z20
D6
D23
D15 Z15 Z7
S11
Z8
Z4Z31
D30
D16 Z18
Z1D42
D38
STZ1
GM14
GM1
Z22
S17
S24 S19 Z12
D13
S13
S22
D35
S8
Z17Z27
S7
Z6
Z28
S10
Z26
D32
S26
Z30D20
D34
Z24
Z23
Z9
S23
S21D26
D28
D24 D7
Z35Z3
D25
STZ2
Z13
Z21
ST3 D21
D19
S25
ST7 D8
ST6
Z25
D17
GM15
D33
D45
15
2
3
4
5
ST9
6
7
GM9
Z10 D14
D27
8
Z14
Z34
Z11
Z32
GM11
Z19
GM12
-2
9
GM6
S27
GM13
ST25
-3
-3
-2
-1
0
1
z_Jv
Landslide risk reduction by coupling
monitoring and numerical modelling
2
3
4

31. AN ALTERNATIVE APPROACH FOR ROCK MASS CLASSIFICATION
3) Factorial analysis (quantification of the proposed rock mass index)
ISD = (0,911 * z_Jv) – (0,911 * z_Ib)
CLUSTER
Jv
Ib (cm)
ISD
N° osservazioni
7
35,7
6,3
3,75
5
0,35
9
Q
6
31,0
7,3
2,83
7
0,31
11
P
2
27,5
9,0
1,90
11
0,26
14
O
9
21,0
6,9
1,60
5
0,52
33
N
5
22,8
9,6
1,12
13
0,18
16
M
11
16,2
8,1
0,64
7
0,32
50
L
13
22,9
12,5
0,34
6
0,31
91
I
1
17,7
11,1
0,00
33
0,39
8337
H
12
12,2
9,9
-0,38
4
0,32
-84
G
14
23,0
17,5
-1,06
2
0,13
-12
F
3
13,0
12,9
-1,11
29
0,28
-25
E
10
17,3
15,4
-1,22
4
0,25
-20
D
4
8,5
15,0
-2,29
11
0,48
-21
C
8
13,4
19,1
-2,77
5
0,38
-14
B
15
9,5
23,4
-4,46
2
0,40
-9
A
Landslide risk reduction by coupling
monitoring and numerical modelling
Deviazione standard ISD Coeff. Variaz. ISD CLASSE di AMMASSO

32. Parametrization of rock mass classes by equivalent continuum approach
1) Hoek & Brown criterion for strength
Inviluppo a rottura di Hoek & Brown - CLASSE A
Percorso tensionale a rottura (Kf line) - CLASSE A
30
14
25
8
)
a
P
M
(
q
10
15
σ1(
)
a
P
M
20
10
Kf line
6
4
5
2
0
-0,5
q = 0,86p + 0,27
R² = 0,99
12
0
0,0
0,5
1,0
1,5
2,0
0
2,5
5
10
σ 3 (MPa)
Classe
Q
P
O
N
M
L
I
H
G
F
E
D
C
B
A
σci (MPa)
32,7
34,5
36,5
37,2
38,2
39,4
40,1
40,9
41,8
43,6
43,7
44,0
46,9
48,3
53,4
15
20
p (MPa)
mi
D
RQD (% )
BRMR
GSI
mb
s
a
Jv
33
33
33
33
33
33
33
33
33
33
33
33
33
33
33
0,95
0,85
0,75
0,70
0,65
0,60
0,55
0,50
0,45
0,40
0,40
0,40
0,25
0,20
0,05
0,0
12,7
24,3
45,7
39,8
61,5
39,4
56,6
74,7
39,1
72,1
57,9
87,0
70,8
83,7
35
37
40
43
44
49
44
49
52
46
52
51
56
55
58
30
32
35
38
39
44
39
44
47
41
47
46
51
50
53
0,2821
0,4833
0,8043
1,0941
1,3086
1,8953
1,6349
2,2930
2,8694
2,3693
3,0970
2,9618
4,4661
4,5375
5,8997
1,14E-05
2,64E-05
6,58E-05
1,25E-04
1,75E-04
4,19E-04
2,49E-04
5,72E-04
9,80E-04
5,19E-04
1,12E-03
9,85E-04
2,63E-03
2,60E-03
4,94E-03
0,52234
0,51953
0,51595
0,51302
0,51217
0,50866
0,51217
0,50866
0,50705
0,51062
0,50705
0,50755
0,50535
0,50573
0,50466
35,7
31,0
27,5
21,0
22,8
16,2
22,9
17,7
12,2
23,0
13,0
17,3
8,5
13,4
9,5
Landslide risk reduction by coupling
monitoring and numerical modelling
φ (°)
28
34
39
42
45
48
47
50
52
51
54
53
56
57
59
c (MPa)
0,16
0,19
0,20
0,22
0,24
0,24
0,27
0,31
0,31
0,32
0,34
0,35
0,41
0,42
0,53
σt (MPa)
-0,001
-0,002
-0,003
-0,004
-0,005
-0,009
-0,006
-0,010
-0,014
-0,010
-0,016
-0,015
-0,028
-0,028
-0,045

33. Parametrization of rock mass classes by equivalent continuum approach
2) Sridevi & Sitharam method for deformability
Ej(σ 3=0) = exp(-1,15*(10E-2)*Jf) * Ei(σ 3=0)
CLASSE di AMMASSO
Jv
Ib (cm)
ISD
"r"
"n"
Jf
Q
35,7
6,3
3,75
5
32,7
0,75
0,4
119
51
13
P
31,0
7,3
2,83
7
34,5
0,76
0,4
102
51
16
O
27,5
9,0
1,90
11
36,5
0,76
0,4
90
51
18
N
21,0
6,9
1,60
5
37,2
0,77
0,4
68
51
23
M
22,8
9,6
1,12
13
38,2
0,77
0,5
59
51
26
L
16,2
8,1
0,64
7
39,4
0,78
0,5
42
51
32
I
22,9
12,5
0,34
6
40,1
0,78
0,5
59
51
26
H
17,7
11,1
0,00
33
40,9
0,78
0,5
45
51
30
G
12,2
9,9
-0,38
4
41,8
0,79
0,6
26
51
38
F
23,0
17,5
-1,06
2
43,6
0,79
0,6
49
51
29
E
13,0
12,9
-1,11
29
43,7
0,79
0,6
27
51
37
D
17,3
15,4
-1,22
4
44,0
0,80
0,7
31
51
36
C
8,5
15,0
-2,29
11
46,9
0,80
0,7
15
51
43
B
13,4
19,1
-2,77
5
48,3
0,82
0,7
23
51
39
A
9,5
23,4
-4,46
2
53,4
0,84
0,7
16
51
42
Landslide risk reduction by coupling
monitoring and numerical modelling
N° osservazioni Qc (MPa)
Ei (σ3 = 0) (GPa) Ej (σ3 = 0) (GPa)

34. Parametrization of rock mass classes by equivalent continuum approach
2) Sridevi & Sitharam method for deformability
Vertical
zoning
CLASSE di
AMMASSO
ISD
Jf
σ3
(MPa)
σci (MPa)
(σ3 =5)
σcj (MPa)
(σ3 =5)
Ej (σ3 = 0)
(GPa)
Ej (σ3 = 5)
(GPa)
Q
3,75
119
5
96,8
37,4
13
15
P
2,83
102
5
96,8
42,8
16
18
O
1,90
90
5
96,8
46,9
18
21
N
1,60
68
5
96,8
56,1
23
27
M
1,12
59
5
96,8
60,3
26
30
L
0,64
42
5
96,8
69,4
32
37
I
0,34
59
5
96,8
60,5
26
30
H
0,00
45
5
96,8
67,3
30
35
G
-0,38
26
5
96,8
78,8
38
44
F
-1,06
49
5
96,8
65,7
29
34
E
-1,11
27
5
96,8
77,7
37
43
D
-1,22
31
5
96,8
75,6
36
42
C
-2,29
15
5
96,8
85,7
43
50
B
-2,77
23
5
96,8
80,3
39
46
A
-4,46
16
5
96,8
85,1
42
49
Landslide risk reduction by coupling
monitoring and numerical modelling
CLASSE di AMMASSO
ISD
Ej (σ3 = 5) (GPa)
Q
3,75
15
ν
0,25
P
2,83
18
0,25
O
1,90
21
N
1,60
27
M
1,12
L
Gj (σ3 = 5) (GPa) Kj (σ3 = 5) (GPa)
6
10
7
12
0,25
8
14
0,25
11
18
30
0,25
12
20
0,64
37
0,25
15
25
I
0,34
30
0,25
12
20
H
0,00
35
0,25
14
24
G
-0,38
44
0,25
18
30
F
-1,06
34
0,25
14
23
E
-1,11
43
0,25
17
29
D
-1,22
42
0,25
17
28
C
-2,29
50
0,25
20
33
B
-2,77
46
0,25
18
30
A
-4,46
49
0,25
20
33

35. The geomechanical model - 1
Landslide risk reduction by coupling
monitoring and numerical modelling

36. The geomechanical model - 2
Applying the continuum equivalent approach to the time
dependent behavior
1) a Burgers visco-plastic model was assumed for the MRS;
2) a Burgers visco-plastic model coupled with a plasticity threshold was assumed for the RL, DRL and Ls.
Viscosity values of MRC: the viscosity values of the Kelvin–Voight visco-elastic element was always assumed
to be one order of magnitude higher than the ones used for the visco-plastic Maxwell element.
For calibrating the viscosity values of RL, DRL and Ls a best fit was performed between the monitored
displacements, referred to the different excavation and re-shaping steps within the landslide mass and the
numerical modeled ones.
Landslide risk reduction by coupling
monitoring and numerical modelling

37. Sequential numerical modeling
Landslide risk reduction by coupling
monitoring and numerical modelling

38. Sequential numerical modeling: results
Landslide risk reduction by coupling
monitoring and numerical modelling

39. Final remarks
1) Integrated monitoring as a tool for better understanding and constraining
the slope instability (refinement of the geological model);
2) Controlling the performance of stabilization countermeasures and
management of emergency phases;
3) Testing the suitability of time of failure prediction based on semi-empirical
models;
4) Successful attempt of integrating equivalent continuum approaches with
visco-plastic constitutive laws;
5) Possible future development: the numerical model validated via backanalysis as a tool for implementing forward analyses, accounting for the
work-related stress variations.
Landslide risk reduction by coupling
monitoring and numerical modelling