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Seismic vulnerability assessment using field survey and Remote Sensing techniques

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Seismic vulnerability assessment using field survey and Remote Sensing techniques …

Seismic vulnerability assessment using field survey and Remote Sensing techniques
Paolo Ricci, Gerardo Mario Verderame, Gaetano Manfredi - Department of
Structural Engineering (DIST) - University of Naples Federico II
Maurizio Pollino, Flavio Borfecchia, Luigi De Cecco, Sandro Martini - National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA, UTMEA-TER)
Carmine Pascale, Elsabetta Ristoratore, Valentina James - Consortium T.R.E. Technologies for Building Rehabilitation

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  • 1. Seismic vulnerability assessment using field survey and Remote Sensing techniques P. Ricci 1 , G. M. Verderame 1 , G. Manfredi 1 , M. Pollino 2 , F. Borfecchia 2 , L. De Cecco 2 , S. Martini 2 , C. Pascale 3 , E. Ristoratore 3 , V. James 3 1 University of Naples Federico II , Department of Structural Engineering (DIST) 2 ENEA - National Agency for New Technologies, Energy and Sustainable Economic Development, “Earth Observations and Analyses” Lab 3 Consorzio TRE - Tecnologie per il Recupero Edilizio "Cities, Technologies and Planning" (CTP 2011) University of Cantabria, Santander - June 20 th -June 23 th , 2011
  • 2. Introduction
    • 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
    June 20, 2011
  • 3. Avellino city
    • Avellino is a 52,700 people city in Campania, Southern Italy
    June 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)
  • 4. Seismic hazard June 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
  • 5. Field survey on building stock
    • 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:
      • Global geometrical parameters (number of storeys, plan morphology, plan dimensions, etc.)
      • Local geometrical parameters (interstorey height, bay length, etc.)
      • Structural typology (masonry, reinforced concrete, etc.)
      • Distribution of infill panels (non-structural elements potentially highly influencing the seismic response)
      • Age of construction
    June 20, 2011
    • Data were collected through a survey form implemented in Tablet PCs
  • 6. Field survey on building stock
    • 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
    June 20, 2011 Pre-1981 Post-1981
    • RC buildings – age of construction :
    Edifici in CA – epoca di costruzione
  • 7. Field survey on building stock June 20, 2011
    • RC buildings – morphology :
    • Structural typology :
  • 8. Field survey on building stock June 20, 2011
    • RC buildings – interstorey height [m]:
    • First storey
    • Upper storeys
    Mean: Median: CoV: 3.48 3.30 0.17 Mean: Median: CoV: 3.11 3.20 0.17
  • 9. Field survey on building stock June 20, 2011
    • RC buildings – bay length [m]:
    • RC buildings – opening percentage in infills at 1 st storey :
    Mean: Median: CoV: 4.50 4.40 0.18
  • 10. Remote 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
    June 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
  • 11. LIDAR technology June 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)
    • 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
  • 12. LIDAR data processing
    • Pre-processing: filtering and georeferencing
    • DTM extraction ( Bare-earth )
    June 20, 2011
    • Building features extraction from non-ground points:
      • Planimetric description;
      • Height values;
      • Roof typology;
      • Etc…
    • Vegetation characterization (optional)
    3D View
  • 13. DTM and DSM extracted from LIDAR June 20, 2011
  • 14. GIS 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
    • 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…)
  • 15. Seismic vulnerability assessment
    • Seismic vulnerability assessment can be carried out by means of empirical or analytical methods:
    June 20, 2011
    • 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
  • 16. Seismic vulnerability assessment - Procedure
    • An analytical method was adopted (Ricci, 2010)
    June 20, 2011
    • 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
      • simulated design procedure to evaluate the building structural characteristics
      • construction of simplified structural model including elements representing the infill panels
      • closed-form evaluation of the lateral non-linear static force-displacement response
      • 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.
  • 17. Seismic vulnerability assessment - Input Data June 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.
  • 18. Seismic vulnerability assessment - Input Data June 20, 2011
    • global geometrical parameters
    • local geometrical parameters
    • distribution of infill panels
    • age of construction
    LIDAR Statistics about building characteristics ISTAT census data
    • for each single building
    • with the highest confidence level
    Field Survey
    • local geometrical parameters
    • distribution of infill panels
    • age of construction
    • global geometrical parameters
    Seismic Vulnerability Assessment Procedure Seismic Vulnerability Assessment Procedure Seismic Risk Seismic 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”)
  • 19. Results based on field survey data June 20, 2011
      • Results of the seismic vulnerability assessment are reported in terms of failure probability (P f ) 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
      • P f at DS5
  • 20. Results based on field survey data June 20, 2011
      • Results of the seismic vulnerability assessment are reported in terms of failure probability (P f ) 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:
      • P f at DS5
  • 21. Results based on field survey data June 20, 2011
      • The spatial distribution of average annual failure probability at DS5 per census cell shows higher values in central and North-Western areas:
      • P f at DS5
  • 22. Results based on field survey data June 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:
      • Stratigraphic conditions
  • 23. Results based on LIDAR data June 20, 2011
      • Results based on LIDAR data can be analyzed by evaluating the “error” with respect to seismic risk estimated with Field Survey data:
      • 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
      ERR PfSD1 ERR PfSD2 ERR PfSD3 ERR PfSD4 ERR PfSD5  +8% +7% +13% +9% +15%  -  -12% -15% -15% -12% -9%  +  21% 23% 37% 24% 39%
      • Error in P f at DS5
  • 24. Results based on LIDAR data June 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 1 st and upper storeys) provided by statistics on building characteristics
      • As expected, an overestimation in N storeys leads to an overestimation in P f , and vice versa
      • Error in P f at DS5
      • Error in N storeys
  • 25. Results based on LIDAR data June 20, 2011
      • A higher seismic risk in central and North-Western areas is observed, again:
  • 26. Results based on LIDAR data June 20, 2011
      • A comparison between spatial distribution of P f according to the two different data sources highlights an acceptable scatter in the identification of highest seismic risk areas
      • Field Survey data
      • LIDAR data
      • good agreement!
  • 27. Conclusions June 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
  • 28.
    • Thank you for your attention
    June 20, 2011

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