seismic vulnerability assessment using field survey and remote sensing techniques

Seismic vulnerability assessment Seismic vulnerability assessment using field survey and Remote using field survey and Remote

Sensing techniquesSensing techniquesP. Ricci1, G. M. Verderame1, G. Manfredi1, M. Pollino2, F. Borfecchia2,

L. De Cecco2, S. Martini2, C. Pascale3, E. Ristoratore3, V. James3

1University of Naples Federico II, Department of Structural Engineering (DIST)

2ENEA - National Agency for New Technologies, Energy and Sustainable Economic Development, “Earth Observations and Analyses” Lab

3Consorzio TRE - Tecnologie per il Recupero Edilizio

"Cities, Technologies and Planning" (CTP 2011) University of Cantabria, Santander - June 20th-June 23th, 2011

"Cities, Technologies and Planning" (CTP 2011) University of Cantabria, Santander - June 20th-June 23th, 2011

Seismic vulnerability assessment using field survey and Remote Sensing techniquesCTP 2011CTP 2011

IntroductionIntroduction

SIMURAI is an Italian project aimed at the definition of integrated tools for multi-risk assessment in highly urbanized areas. It was developed within the case study city of Avellino (Southern Italy)

Field survey and seismic hazard evaluation

Airborne LIDAR data acquisition and processing

Seismic vulnerability assessment

In this work the seismic vulnerability assessment carried out on Reinforced Concrete (RC) buildings is presented

The specific seismic hazard was evaluated for the Avellino city based on seismological studies

The seismic risk in terms of failure probabilities (in a given time window and for given building performance levels) was calculated

Different data sources – namely the Field Survey and the airborne LIDAR data, characterized by different detail level and time demand – were assumed to define the input data to seismic vulnerability assessment procedure; hence, results of a multilevel seismic vulnerability assessment are compared and discussed

2June 20, 2011


Avellino cityAvellino city

Avellino is a 52,700 people city in Campania, Southern Italy

3June 20, 2011

It is in a high seismic area: it was struck strongly by the disastrous Irpinia earthquake of 23 November 1980, measuring 6.89 on the Richter Scale (2,914 people killed and more than 80,000 injured)

From 2006 the urban planning issues of Avellino and neighbor areas are regulated by two instruments: P.I.C.A. (Italian acronym that stands for integrated Project for Avellino City) and P.U.C. (Urban Plan for Avellino Municipality)


Seismic hazardSeismic hazard

4June 20, 2011

Seismic input was evaluated for Avellino city based on the Italian National Technical Code, providing seismic hazard for the entire national territory

Seismic Hazard is expressed in terms of PGA (Peak Ground Acceleration) and elastic acceleration response spectra, providing the seismic input for a structure as the maximum response of an equivalent Single Degree Of Freedom oscillator

Parameters defining these spectra are given as a function of site coordinates and return period of the earthquake

Seismic input was properly amplified to take into account local topographic and stratigraphic conditions, respectively determined from microzonation data and by spatial processing the Digital Terrain Model of the city in order to obtain the slope surface at any point

Stratigraphic conditions Topographic conditions


Field survey on building stockField survey on building stock

5June 20, 2011

A Field Survey was carried out on building stock aimed at gathering detailed data about building characteristics to be employed in the vulnerability assessment, namely:

1. Global geometrical parameters (number of storeys, plan morphology, plan dimensions, etc.)

2. Local geometrical parameters (interstorey height, bay length, etc.)

3. Structural typology (masonry, reinforced concrete, etc.)

4. Distribution of infill panels (non-structural elements potentially highly influencing the seismic response)

5. Age of construction

6. …

Data were collected through a survey form implemented in Tablet PCs



6June 20, 2011

1327 buildings were surveyed in the area of the Municipality. Out of these, 1058 are RC building, resulting in about 80% of the building population

Pre-1981 Post-1981

0%

10%

20%

30%

40%

50%

Edifici in CA – epoca di costruzione

RC buildings – age of construction:



7June 20, 2011

0%10%20%30%40%50%60%70%

RC buildings – morphology:

Structural typology:

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

RC Masonry Steel Mixed



8June 20, 2011

RC buildings – interstorey height [m]:

• First storey

0%10%20%30%40%50%60%70%80%90%

0%10%20%30%40%50%60%70%80%90%

• Upper storeys

Mean:Median:CoV:

3.483.300.17

Mean:Median:CoV:

3.113.200.17



9June 20, 2011

RC buildings – bay length [m]:

0%

5%

10%

15%

20%

25%

30%

RC buildings – opening percentage in infills at 1st storey:

0%

5%

10%

15%

20%

25%

Mean:Median:CoV:

4.504.400.18


Remote Sensing data and techniquesRemote Sensing data and techniques Remote Sensing (RS) data and techniques are the main source of a

wide range of information about urbanized areas

RS advantages: cost effectiveness and timeliness

RS data give a strong support in monitoring tasks and are essential for an effective and sustainable urban planning and management

Gathering information about buildings 3D geometry (height, plan morphology and dimensions) is fundamental for extensively evaluating the vulnerability

10June 20, 2011

A specific methodology has been implemented and calibrated to extract 3D buildings parameters using RS data acquired by means of active LIDAR (Light Detection and Ranging) technology, which allowed to assess the height and planimetric shape of buildings

LIDAR is an effective technology for the acquisition of high quality Digital Surface Models (DSM) and Digital Terrain Model (DTM), due to its ability to generate 3D dense terrain point cloud data with high accuracy


LIDAR technologyLIDAR technology

11June 20, 2011

LIDAR airborne RS mission has been planned and carried out over the entire municipality of Avellino, using an Optech ALTM 3100 system and acquiring range point clouds data with a density of 4 points for square meter

The 3D geometric parameters of buildings were extensively obtained through a methodology integrating active LIDAR technology (from point clouds) and GIS techniques (spatial analysis)

SYSTEM ALTM 3100 ALTM 3033 ALTM 1020 TopoSys TopoEye I ScaLars FliM apM anufacturer Optech Optech Optech TopoSys Saab Stutgard

Univ.Fugro

Country Canada Canada Canada Germany Sweden Germany HollandReflectance Si Si No No Si Si SiWave lenght 1064 1064 1047 nm 1535 nm 1064 nm 1064 nm -Scan type Pulse Pulse Pulse Pulse Pulse Continuous Pulse

Flight height 80-3500 265-3000 1000 m 850 m 500 m 750 m 100 mAircraft speed - - - 70 m /s 10-25 m /s - -Pulse repetitionrate

33-100 Khz 33 Khz 5000 Hz 8000 Hz 6000 Hz 7000 Hz 8000 Hz

Scan frequency up to 70 Hz up to 70 Hz 50 Hz 630 Hz 650Hz - 40 Hz

FOV Up to 25° Up to 20° Up to 20° 7° Up to 10° 14°-20° 30°Swath up to 0.93 H Up to 700 m Up to 700 m 230 m Up to 168

m- 70 m

Operated on Helicopter Helicopter Aircraft Aircraft Helicopter Aircraft Helicopter

The exploitation of GIS and RS techniques coupled with tailored ground calibrations of above described procedures has allowed a detailed estimation of geometric and typological attributes for each building in the areas, in order to support the vulnerability assessment


LIDAR data processingLIDAR data processing

Pre-processing: filtering and georeferencingDTM extraction (Bare-earth)

June 20, 2011 12

Building features extraction from non-ground points:

• Planimetric description;

• Height values;

• Roof typology;

• Etc…Vegetation characterization (optional)

3D View


DTM and DSM extracted from LIDAR DTM and DSM extracted from LIDAR

June 20, 2011 13


GIS proceduresGIS procedures

Combining digital Cartography (1:2,000 scale) and height values coming from LIDAR, for each building geometric attributes and morphological features have been extracted in a semi-automatic way: area, perimeter, volume, total height of the building and ground altitude beneath itself

June 20, 2011 14

The updated version of Cartography constituted the basis of the GeoDatabase, suitably designed to be included in DSS tools/procedures devoted to support planning and decision making in case of different risk scenarios

Finally, the updated Cartography has been overlaid with other GIS layers data, in order to enrich information about buildings (geometry, typology, construction age, etc…)


Seismic vulnerability assessmentSeismic vulnerability assessment

15June 20, 2011

Seismic vulnerability assessment can be carried out by means of empirical or analytical methods:

in empirical methods the assessment of expected damage for a given building typology is based on the observation of damage suffered during past seismic events

• Damage Probability Matrices (e.g. EMS-98)

• Continuous vulnerability curves

• Vulnerability Index method

• Screening methods

in analytical methods the relationship between seismic intensity and expected damage is provided by a model with direct physical meaning

• Cosenza et al., 2005; DBELA (Pinho et al., 2002)

• HAZUS

• …


Seismic vulnerability assessment - Seismic vulnerability assessment - ProcedureProcedure

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An analytical method was adopted (Ricci, 2010)

0 0.02 0.04 0.06 0.08 0.1 0.12 0.140

0.5

1

1.5

2

2.5

Top displacement in X direction [m]

Sa

e(T

eff)

[g]

The method includes the following steps to determine the seismic capacity of a RC building:

The statistical characterization of input parameters assumed as Random Variables allows the evaluation of fragility curves for each building

1. simulated design procedure to evaluate the building structural characteristics

2. construction of simplified structural model including elements representing the infill panels

3. closed-form evaluation of the lateral non-linear static force-displacement response

4. assessment of seismic capacity within the framework of the N2 method (Fajfar, 1999) Displacement capacity is evaluated according to EMS-98 damage scale

Ricci P., 2010. Seismic vulnerability of existing RC buildings. PhD thesis, University of Naples Federico II.


Seismic vulnerability assessment - Input Seismic vulnerability assessment - Input DataData

17June 20, 2011

Input data of the adopted methodology:

global geometrical parameters (height and plan dimensions)

local geometrical parameters (interstorey height and bay length)

distribution of infill panels

type of design and values of allowable material stresses employed in the simulated design procedure (*)

material characteristics (*)

(*) from literature, assumed as depending on the age of construction

Ricci P., 2010. Seismic vulnerability of existing RC buildings. PhD thesis, University of Naples Federico II.


Seismic vulnerability assessment - Input Seismic vulnerability assessment - Input DataData

18June 20, 2011

1.global geometrical parameters

2.local geometrical parameters

3.distribution of infill panels

4.age of construction

LIDAR

Statistics about building

characteristicsISTAT census data

for each single building

with the highest confidence level

Field Survey

2.local geometrical parameters

3.distribution of infill panels

4.age of construction

1.global geometrical parameters

Seismic Vulnerability Assessment Procedure

Seismic Vulnerability Assessment Procedure

Seismic RiskSeismic Risk

Comparison

LIDAR data about global geometrical parameters of single buildings (1) are integrated by a priori information about remaining building

parameters (2,3,4), which are assumed as Random Variables

(“reference”) (“approximated”)


Results based on field survey dataResults based on field survey data

19June 20, 2011

Results of the seismic vulnerability assessment are reported in terms of failure probability (Pf) at different Damage States (i.e., performance levels) in a time window of 1 year

Evaluated failure probabilities at can be reported as a function of the number of storeys:

Vulnerability clearly increases with the number of storeys

Pf at DS5



20June 20, 2011

Results of the seismic vulnerability assessment are reported in terms of failure probability (Pf) at different Damage States (i.e., performance levels) in a time window of 1 year

If mean failure probabilities in pre- and post- 1981 buildings are compared, a higher vulnerability in pre-1981 buildings, as expected, can be generally observed:

Pf at DS5



21June 20, 2011

The spatial distribution of average annual failure probability at DS5 per census cell shows higher values in central and North-Western areas:

Ü

0 390 780 1 170 1 560195Meters

0.000033 - 0.000060

0.000061 - 0.000120

0.000121 - 0.000180

0.000181 - 0.000240

0.000241 - 0.000300

Pf at DS5



22June 20, 2011

A clear influence of the difference in seismic hazard due to a different soil type can be recognized, leading, as expected, to higher failure probabilities for buildings located on less stiff soil:

Ü

0 500 1 000 1 500 2 000250Meters

B

C

E

Stratigraphic conditions


Results based on LIDAR dataResults based on LIDAR data

23June 20, 2011

Results based on LIDAR data can be analyzed by evaluating the “error” with respect to seismic risk estimated with Field Survey data:

ERRPfSD1 ERRPfSD2 ERRPfSD3 ERRPfSD4 ERRPfSD5 +8% +7% +13% +9% +15%

- -12% -15% -15% -12% -9%+ 21% 23% 37% 24% 39%

Error in Pf at DS5

Generally speaking, a risk overestimation has to be expected when data employed in the assessment procedure are characterized by a

lower knowledge level, that is, by a higher uncertainty



24June 20, 2011

Results based on LIDAR data can be analyzed by evaluating the “error” with respect to seismic risk estimated with Field Survey data

Such error can also be reported as depending on the error in the evaluation of number of storeys from LIDAR data:

Based on total height provided by LIDAR, number of storeys for each building is calculated as the value leading to the least scatter with median values of interstorey height (at 1st and upper storeys) provided by statistics on building characteristics

As expected, an overestimation in Nstoreys leads to an overestimation in Pf, and vice versa

Err

or

in P

f at

DS5

Error in Nstoreys



25June 20, 2011

A higher seismic risk in central and North-Western areas is observed, again:

Ü

0 390 780 1 170 1 560195Meters

0.000033 - 0.000060

0.000061 - 0.000120

0.000121 - 0.000180

0.000181 - 0.000240

0.000241 - 0.000300



26June 20, 2011

A comparison between spatial distribution of Pf according to the two different data sources highlights an acceptable scatter in the identification of highest seismic risk areas

Ü

0 390 780 1 170 1 560195Meters

0.000033 - 0.000060

0.000061 - 0.000120

0.000121 - 0.000180

0.000181 - 0.000240

0.000241 - 0.000300

Ü

0 390 780 1 170 1 560195Meters

0.000033 - 0.000060

0.000061 - 0.000120

0.000121 - 0.000180

0.000181 - 0.000240

0.000241 - 0.000300

Field Survey data LIDAR data

good agreement!


ConclusionsConclusions

27June 20, 2011

A generally acceptable scatter was observed and the same areas were identified as the most exposed to seismic risk (most important in large scale assessment)

The methodology for extracting building parameters from LIDAR data can be certainly improved (e.g., taking into account the presence of inclined roofs or partly underground storeys when the number of storeys is evaluated from building height)

A multilevel seismic vulnerability assessment was carried out on RC buildings in Avellino city based on an analytical methodology, assuming two different sources for input data:

• Field Survey, leading to “reference” results

• Airborne LIDAR (integrated with census data and statistics about building characteristics)

Future developments: data mining models for the identification of structural typology may be implemented and verified

LIDAR seems to be a promising cost effective and relatively fast option in providing data to Decision Support System for strategic territorial planning in seismic risk management


Thank you for your attention

28June 20, 2011

seismic vulnerability assessment using field survey and remote sensing techniques

Technology

building stock

input data

rc buildings morphology

microzonation data

detailed data

building population

remote sensing data

earthquake seismic input