A glance at the LEWIS Project - WebGis...

AA glanceglance at the LEWIS Projectat the LEWIS ProjectA A glanceglance at the LEWIS Projectat the LEWIS Project

Giovanna Capparelli & Pasquale Versace

Presentation Outlines

LEWIS Projectj“Integrated systems for

hydrogeological risk monitoring, early warning hydrogeological risk monitoring, early warning and mitigation along the main lifelines”

Landslides triggered by rainfallM h i l M d l i h LEWI P jMathematical Models in the LEWIS Project Application to real casespp

Lewis project: General Purposes

Providing an efficient response to the problem oflandslides forecasting that may affect the roads.f g m y ff .

Allowing the enhancement of services supporting mobility.

Promoting service innovation in environmental monitoring field.

Lewis project: Components

L d hiLeadership

Actuator

COMPONENT OF THE PROJECT (1)

“Geology, geomorphology and landslide susceptibility along a highway gy, g p gy p y g g ysection in northern Calabria” F. Scarciglia


DISPLACEMENT MEASUREMENTS

AREAL MONITORING SYSTEMS

L-Band radar system GB Interferometric radar

“Radar systems for landslides early warning”G. Di Massa

“Landslides Monitoring”N. Casagli


DISPLACEMENT MEASUREMENTS

PUNCTUAL DISPLACEMENT SYSTEMS

The SWAN network Position and inclination syst.

“SWAN Smart Wireless Accelerometer Network for Landslide Monitoring”

“POIS: a position and inclination sensor for the monitoring of slopes and structures”f g

P. Orabonag f p

G. Artese

COMPONENT OF THE PROJECT (4)DISPLACEMENT FORECASTINGDISPLACEMENT FORECASTING

MODELS

Complete model- SUSHI

Areal model – GEOTOP

Model of mud-flowi SCIDDICA propagation SCIDDICA 1

SLOW LANDSLIDE FAST LANDSLIDE

Brazil,to(AV)

, 2011Mon

tagut

M

DISPLACEMENTDISPLACEMENTFORECASTINGMEASUREMENTS

measurement of correlated parameters

AREAL MONITORING SYSTEMSPUNCTUAL DISPLACEMENT SYSTEMS MODELS


TTRANSMISSION AND DATA ACQUISITION

Data transmission network The nodes of the transmission network.

SD

Cisco 1720

BRIS/T

CONSOLE

AUXWIC 0 OK

OK

B2B1

WIC 1 OK

DSUCPU

LNK100FDX

S3

LOOP

LP

“Flexible FPGA implementation to extensive area monitoring on early warning systems”g y g yH. Havancini


DATA COLLECTING AND PROCESSING CENTER(CAED)( )

COMPONENT OF THE PROJECT (7)INTERVENTION MODELINTERVENTION MODEL

“Event and risk scenarios”P. Versace


C C R N (CCC)CONTROL CENTER FOR ROAD NETWORK (CCC)

“Landslide Early Warning driving Road Network Management”F. Paoletti

DECISION MAKING

Logical management process

INTERVENTION MODEL

PUNCTUALMONITORING

AREALMONITORING

DATA

Data transmission

WARNING ISSUES –WARNING LEVELS CCC

MONI ORINGDATA

network

ACTIONSMETEOROLOGICAL & HYDROLOGICAL DATA

PROBABILITYOCCURRENCEBY MODELSBY MODELS


EXPERIMENTAL ACTIVITIESEXPERIMENTAL ACTIVITIES

A16 Candela LacedoniaA16 Candela - Lacedonia

A3 Cosenza AltiliaA3 Cosenza - Altilia

A18 Messina RoccalumeraA18 Messina - Roccalumera

A3 SA‐RC

MANCARELLI

Geotechnical

Meteorological

CosenzaTAS A3

HydrologicalMathematical

Altilia

FIEGO

GeotechnicalPOIS

SMAMID

GARCITO‐PIANO D’INFANTE

Hydrological

SMAMID

Mathematical


DISPLACEMENT FORECASTINGMODELS

Complete model- SUSHI

Models Areal model – GEOTOP

Model of mud-flowti SCIDDICA propagation SCIDDICA 1

SUSHI MODELSaturated Unsaturated Simulation for Hillslope InstabilitySaturated Unsaturated Simulation for Hillslope Instability

MAIN FEATURES

applicability to :layered soilslayered soilsirregular shape domainvariable boundary conditions

analysis of the saturated and unsaturated flows

variable boundary conditions

analysis displacement, strain and stress under the effect of rainfall infiltration

SUSHI MODEL:GENERAL FRAMEWORK

Hydrologicalproperties

SWCC, HCF

Hydro-mechanicalproperties

SSCC

Suction StressCharacteristicCurve (SSCC)

Analysis and Forecasting GeotechnicalHydraulic Forecasting LandslidesmoduleModel

SUSHI MODEL:SOFTWARE STRUCTURE


Pre-Processing Post- ProcessingProcessing

GEOMETRY AND STRATIGRAPHY OF ANALYZED AREA

INPUT DATA

MESH CREATION

INPUT DATA



h-refinementsCharacterization

finite element

f

p-refinements

. tetraedro lin. . tetraedro parab.

Galerkin method (FEM)

Decoupling ofNumericall ti

Galerkin method (FEM)

Di ti i t lVariablesresolution Discretizzazione temporale

Iterative solution process



DISPLAYING DISPLAYING RESULTS

DATA COLLECTING CENTER

SUSHI MODEL:SOFTWARE WINDOWS

Real Time

Forecasting +3 hours

SUSHI‐FEM PREVISIONE W

+6 hours nfall

ted

Rain

+12 hours

Fore

cast

F

APPLICATION TO A REAL CASE:TORRE ORSAIA LANDSLIDE

SLOPE SECTION

Altered cover layerInclinometer1 & Piezometer

yLayer_residual strength

Marly Clay

STRATO (kN/m3) c’ (kPa) ’ (°) ψ(°) E (kPa) v

Altered Cover 19 0 27 0 7000 0.3

Layer_residual strength 19 0 18 0 7000 0.3

Marly Clay 20 20 27 0 20000 0.3

Displacements measured along the vertical inclinometer

The inclinometer 1 provides the depth of the failure surface, at about 9 meters.

The instrument (placed on 23-Dec-13) showed a displacement value of 1.8 cm on 4-The instrument (placed on 23 Dec 13) showed a displacement value of 1.8 cm on 4Feb-14, and 3 cm on 20-Mar-14

Discretization adopted

Initial ConditionDec, 2014

04/02/2014Run – Simulations

06/03/2014

20/04/2014

01/05/2014

Run – Simulations

DisplacementDisplacement

Comparison between measured and simulated displacements

March

February

ailu

re

m)

February,2014

March,2014 Fa

men

tes

(mD

ispl

acem

D

Time steps

Global stability analysis

reFa

ilur

Groundwater T Initial cond. 23/12/13 4/02/14 6/03/14 20/04/14

Groundwater T. Ground surface

W.G

GEOTOP: IMPROVEMENTSRigon et al, 2006

• spatially distributed• it models:• it models:

- subsurface saturated and unsaturated flows- surface runoff

Formetta et al., IWL 2013

surface runoff- turbulent fluxes across the soil-atmosphere interface.

GEOTOP: IMPROVEMENTS (1)Rigon et al, 2006

GIS-Jgrass Integration • Computation of input

raster maps• Visualization of outputraster mapsraster maps


Model integration goals n.1: enjoy the GIS uDig-JGrass

Create the input maps: dem soil-type Create the input maps: dem, soil type, slope, aspect, curvatures

Visualize model results

Trento 17 June 2011G. Formetta, Trento 24 June 2011Leipzig 05 July 2012G. Formetta, Formetta G., ARS‐USDA‐Fort Collins (CO)Formetta et al., IWL 2013

GEOTOP: IMPROVEMENTS (2)

OMS Integration• Use of automatic

l b calibration algorithms such as PSO, LUCA, DREAMDREAM

GIS-Jgrass Integration • Computation of input

raster maps• Visualization of outputraster maps


raster maps

Model integration goals n.2: enjoy calibration algorithms

Use the OMS3 optimization algorithm f r p r m t r stim ti n:for parameter estimation:

- Multisite calibration

37Trento 17 June 2011G. Formetta, Trento 24 June 2011Leipzig 05 July 2012G. Formetta, Formetta G., ARS‐USDA‐Fort Collins (CO)

- Multidata calibration



Time and space varying probabilistic infinite slope S.F.


REAL TIME Work-flow

01/07/09 10:00 03/11/09 10:00 03/12/09 10:00

mm

] GEOtop Application: REAL TIME Work-flow

timeon

[m

50 cm

Suct

i

100 cmDep

th

150 cm

1

GEOtop Application: A3 SA-RC, Test Site – Fiego (CS)

2

Model of mud-flow propagation SCIDDICA 1

Cellular Automata model for flow‐like landslides simulation

M.V. Avolio, S. Di Gregorio, V. Lupiano, G.A. Trunfio,

• SPACE is a hexagonal tassellation CA, whosecells enclose a computing unit.

• Cell STATE is composed by sub-states :A is the cell altitude, KH is the debris kinetic headD is the depth of erodable soil coverD is the depth of erodable soil coverTH is the thickness of debris inside the cellX , Y are the co-ordinates of debris mass centreM is the momentumM is the momentumF6 the debris flows toward the six adjacent cells with own TH, KH, X, Y, M.

Model of mud-flow propagation SCIDDICA 1

improvementsI i ibl i d l i h l i d idIt is possible to introduce alterations to morphology in order to considerpossible human works (embankments, canals, wall, bridge, etc.).

Prediction of a future scenario in b f di l k

Effect of containment wall in the absence of remedial works. previous case.

SOPRA URNO DEBRIS FLOWS SIMULATION

Sopra Urno debris flow caused the largest number of casualtiesp gand damages, due to the fact that the flow crossed the village. The simulation shows a good capability of the model to

describe the debris run-out, in particular, in high zone ofslope.

In the urbanized area, differences are noted with path of thereal event, especially in lateral streets, but the result can beconsidered acceptableconsidered acceptable.

Comparison of simulated and real-event (Fig. 4) return avalue of fitness f = 0.74 (Table 2, case 2).

The maximum velocities reached by simulated flows (Fig. 5 y ( ga) are high, as expected, in the steeper areas, and decrease gradually at the outlet in downstream.

Fig. 4: comparison between Sopra Urnocreek debris flows and simulated event

T bl 2Table 2case R (m2) S (m2) f

1 11785,76 15924,21 0,732 19476,87 28066,64 0,743 14168 38 22374 40 0 773 14168,38 22374,40 0,774 9207,63 17049,25 0,705 3768,42 6936,00 0,726 8934,52 13667,88 0,78

Fig. 5: a) maximum velocities; b) maximum detrital thickness; c) eroded regolith.

GIAMPILIERI DEBRIS FLOWS SIMULATION

Comparison between real debris flows and simulated events. a) Loco creek debris flow; b) Punctual creek debris flow; c) and d)Primary school debris flows; d) east of primary school debris flow e) debris flow at East Area. (Respectively 1, 3, 4, 5, 6 in Fig. 2)

Work group – ModelsGabriella La Sala

Giuseppe Formetta

Mirko Vena

Gabriella La Sala

Antonio Donato

Maria V. Avolio

Thomas Zaffino

ConsiderationsConsiderations

The Ews is a sector in great development The Ews is a sector in great development Promoting more and more research activities and

i t ti I t di i li St diexperimentation, Interdisciplinary Studies

For analyzing of the complex phenomena need to put together :

Field analysisDisplacement MonitoringAnalytical modeling of the phenomenonAnalytical modeling of the phenomenon

It takes coordination initiatives

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