30/06/2012 1

Ivan Cipriani

Master II livello in Analisi e Mitigazionedel Rischio Idrogeologico

Lecture on forecasting the failure time of landslides

CREEP

(Terzaghi 1950)

(Varnes 1982)

(Saito & Uzewa 1961)

More than 80 samplein triaxial test

Results of field measurement ofcollapse of a large retaining wall on theOoigawa Railroad (Saito 1965).

Creep secondary

(Saito 1969)

1=relative displacement between two measured pointsl0=initial distance between two measured points

Creep tertiary

Time1/

Velo

city

FAILURE!

Linear regression

(Fukuzono 1985)

Semi-empirical approaches for landslide: time of failure prediction

Creep tertiary

Sviluppo e implementazione di metodologie innovative di monitoraggio per la previsione di frana.

Creep tertiary

Creep tertiary first stage

Creep tertiary

Azimi et al. (1988) have proposed a new graphical method based an observationalsettlement prediction of one-dimensional consolidation proposed by Asaoka (1978). Thismethod is equivalent to Saito’s and Fukuzono’s method for the tertiary creep (Eq. 5 fora=2).

for α>1 e α≠2A e α constantstf=failure time Ωf=spostamento a tf

Integral

(Voight 1988, 1989)

Creep secondary and tertiary

Integral

(Rose and Hungr, 2007 )

Metodology

Case study

A’

15/01/10 17.00 16/01/10 9.00

Landslide monitoring

Bozzano F, Mazzanti P, Prestininzi A (2008) A radar platform for continuous monitoring of a landslide interactingwith an under-construction infrastructure. Italian Journal of Engineering Geology and Environment. 2:35-50.

Case study

Landslides Dataset: 10Period: January 2008 - September 2011

Volume: 101 - 104 m3

Thickness: 1- 3 mType of movement: rotational/translation slideType of material: weathered gneiss, colluvium,spritz beton

Total time span: from few hours to two weeksTotal displacement : from few cms to 1 mPeak of velocity: form 8 mm/s to 66 mm/sPeak of acceleration: from 1mm/h2 to 100 mm/h2

Landslides dataset

30/06/2012Sviluppo e implementazione di metodologie innovative di monitoraggio per la previsione di frana.

Decelerazione pre-rottura!

Bozzano, F., I. Cipriani, P. Mazzanti, and A. Prestininzi (2011), Displacement patterns of a landslide affected byhuman activities: insights from ground-based InSAR monitoring, Natural Hazards, 59(3), 1377-1396, doi:10.007/s11069-011-9840-6.

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Preliminary results

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Preliminary results

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= 0,720

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!/vel

ocity

(mm

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1/ve

loci

tà (m

m/o

ra)-

1

Preliminary results: Fukuzono method

Step by step back analysis of prediction accuracy

Time

1/Ve

loci

ty

FAILURE!

LINEAR FUKUZONO APPROACH!

Time

1/Ve

loci

ty

FAILURE!

Step by step back analysis of prediction accuracy

LINEAR FUKUZONO APPROACH!

Time

1/Ve

loci

ty

FAILURE!

Step by step back analysis of prediction accuracy

LINEAR FUKUZONO APPROACH!

Time

1/Ve

loci

ty

FAILURE!

Step by step back analysis of prediction accuracy

LINEAR FUKUZONO APPROACH!

Time

1/Ve

loci

ty

FAILURE!

Step by step back analysis of prediction accuracy

LINEAR FUKUZONO APPROACH!

Time

1/Ve

loci

ty

FAILURE!

Step by step back analysis of prediction accuracy

LINEAR FUKUZONO APPROACH!

Time

1/Ve

loci

ty

FAILURE!

Step by step back analysis of prediction accuracy

LINEAR FUKUZONO APPROACH!

Time

1/Ve

loci

ty

FAILURE!

Step by step back analysis of prediction accuracy

LINEAR FUKUZONO APPROACH!

Time

1/Ve

loci

ty

FAILURE!

Step by step back analysis of prediction accuracy

LINEAR FUKUZONO APPROACH!

Step by step estimation of prediction accuracy:computed error of the whole dataset

ADF (Average Data Fukuzono) method

Landslide n.2

ADF AVERAGE ADF MOVING AVERAGEFUKUZONO

Mazzanti P, Bozzano F, Cipriani I, Esposito F (2011) Temporal prediction of landslides failure by continuousTInSAR monitoring. 8th International Symposium on Field Measurements in GeoMechanics. 12-16 Settembre2011 Berlino. (In press)

)1()2(

)1(ff

)1()2(

1f

)tt(*)1(*A

t*)1(*A*

)2(*A1

)1()2(

)1()2(

ff

t*)1(*A

t*)1(*A*

)2(*A1

e ≠

Landslide Data Time of failure ( tf) Velocity Failure displacement A R2

Computed (recorded) Computed (recorded) Computed (recorded)

hour mm/hour mm

Landslides affected by small excavation covered by spritz-beton

3 109 9.00 (9.00) 9.82 (9.81) 19.02 (20.30) 1.0000 0.5107 0.9930

9.00 (9.00) 10.45 (9.81) 20.29 (20.30) 0.7321 0.7775 0.9876

4 1893 7.88 (7.88) 37.32 (37.32) 23.31 (23.26) 1.0000 1.6004 0.9453

7.88 (7.88) 39.43 (37.32) 23.26 (23.26) 0.9204 2.1353 0.9549

5 503 20.91 (20.92) 8.08 (8.08) 18.57 (21.85) 1.3456 0.3108 0.9067

20.91 (20.92) 11.29 (8.08) 21.84 (21.85) 0.9064 0.6411 0.9521

6 598 49.75 (49.75) 10.63 (10.63) 64.03 (63.96) 1.1491 0.1363 0.9920

49.75 (49.75) 8.33 (10.63) 63.94 (63.96) 0.8184 0.1627 0.9851

8 151 12.55 (12.55) 27.21 (27.21) 96.44 (99.23) 1.0000 0.2729 0.9895

12.55 (12.55) 27.53 (27.21) 99.23 (99.23) 0.6217 0.7381 0.9937

Average 561 20.02 (20.02) 19.87 (18.61) 45.74 (45.72) 0.8659 0.8857 0.9761

Landslides affected by small excavation not-covered by spritz-beton

1 2189 182.35 (182.33) 17.07 (17.07) 142.38 (144.61) 142.38 0.0618 0.9960

182.35 (182.33) 10.70 (17.07) 144.59 (144.61) 144.59 0.0847 0.9546

7 829 69.01 (69.00) 29.96 (29.96) 108.37 (105.13) 108.37 0.0560 0.9507

69.01 (69.00) 8.02 (29.96) 105.12 (105.13) 105.12 0.1026 0.9195

9 4741 395.04 (395.00) 44.37 (44.37) 960.42 (906.33) 960.42 0.0157 0.9918

395.04 (395.00) 23.21 (44.37) 906.24 (906.33) 906.24 0.0333 0.9773

10 332 27.59 (27.58) 64.06 (64.06) 150.92 (147.88) 150.92 0.2089 0.9928

27. 59 (27.58) 46.21 (64.06) 147.86 (147.88) 147.86 0.4609 0.9892

Average 2023 168.50 (168.48) 38.86 (38.87) 340.52 (325.99) 340.52 0.0856 0.9828

Landslide not affected by excavation

2 4238 353.08 (353.08) 11.75 (13.23) 843.25 (769.25) 1.0000 0.0138 0.9925

353.05 (353.08) 8.31 (13.23) 769.33 (769.25) 0.6449 0.0169 0.9946

i) higher R2 value;

ii) higher similarity between the modelled andmeasured time series of displacement(based on the authors experience) in caseof R2 difference lower than 0.020

iii) computation of a value by the Cornelius andVoight (1995) approach based on theinclination of the linear regression of data inthe velocity vs. acceleration diagram

Table parameters A and α of the entiredataset of landslides

Genetic algorithms

for α>1 e α≠2A e α constantstf=failure time Ωf=displacement a tf

Double integralVoight 1988

Crosta e Agliardi 2003

for α>1 e α≠2A e α constantstf=failure time Ωf=displacement a tf

Double integralVoight 1988

1st order of anchored bulkhead

2nd order of anchored bulkhead

Landslide of March 2007

Inclinometric monitoring

3rd order of anchored bulkhead

Tunnel excavation

Semi-empirical forecasting method based on the tertiary creep theory

Bozzano F, Cipriani I, Martino S, Mazzanti P, Prestininzi A (2011a). Forecasting methods for landslides interactingwith infrastructures. Second World Landslide Forum. 3 - 9 September 2011 Rome. (In press)

Fukuzono (1985)

Method to forecast the timeof failure for landslides thatdid not reached thecollapse

Excavation phase

Period of excavation Excavation f f A tf R2 Monitoring data

meter mm/hour mm hour

First 17/11/2009-01/12/2009 6-12 4.13 36.90 0.81 0.13 54.63 0.9609 Topographical

Second 11/01/2010-15/01/2010 12-17 5.00 38.08 0.87 0.14 69.91 0.9524 TInSAR

Third 26/01/2010-28/01/2010 22-28 2.67 44.65 1.01 0.06 94.88 0.9864 TInSAR

BOZZANO F, CIPRIANI I, MAZZANTI P, (2012) Assessing of failure prediction methods for slope affected by human activities. 11th International & 2nd North American Symposium on Landslides. Alberta, Canada 2 – 8 June 2012 (In press)

A and α parameters

L. 1

L. 7

L. 9

L. 2

Landslides without spritz‐beton77.5 0.29

Hours before max cross‐value Normalized time max cross‐value

Graphs of precipitation and displacement of four landslides

L. 3

L. 6

L. 8

L. 4

Hours before max cross‐value Normalized time max cross‐valueLandslides with spritz‐beton1.3 0.09

CIPRIANI I, MAZZANTI P, (2012) Analisi del comportamento deformativo pre-rottura di frane superficiali tramite monitoraggio con Interferometria SAR Terrestre. IV Congresso Nazionale AIGA. Perugia 6-7 February. (Extended Abstract)

Cross-correlation of precipitation and displacement