Evaluation of seismic vulnerability: current status and the potential use of Remote Sensing
Evaluation of seismic vulnerability: current status and the potential use of Remote Sensing EARTHQUAKE SCENARIO BUILDING VULNERABILITY CURVES DEFINITION CURRENT METHODOLOGY POTENTIAL USE OF REMOTE SENSING > 2
Seismic risk scenario ARMAGEDOM Generation of a probable seismic event Seismic risk scenario Estimate the vulnerability of exposed elements (buildings) Simulation: evaluate consequences of a probable seismic event > 32
Seismic risk scenario BRGM Armagedom software D4 D5 Commune de Lambesc D1 D2 D3 D4 D5 D0 - D1 D3 D2 Scénario départemental de risque sismique, Bouches du Rhône, Sedan et al., 2007 > 4
Seismic vulnerability assessment 1. Empirical approach, based on observation of damages due to past earthquakes observations, optical image correlation Example: estimation of building damages after the 2003 Bam Earthquake (M. de Michele, N. Baghdadi, BRGM, 2006) 2. Mechanical approach, based on analysis of mechanical behaviour of buildings, with cross validation on observations. > 5
Mechanical approach used for vulnerability assessment > Vulnerability curve: continuous functions expressing the probability of exceeding a given damage state, given a function of the earthquake intensity Mean damage Grade 5 4 3 2 1 VI inf VI VI sup 0 5 6 7 8 9 10 11 12 EMS 98 Intensity I + 6. 25 VI - 13. 1 µ D = 2. 5 1 + tanh 2. 3 After RISK-UE, 2003 Mean damage ( d ) as a function of intensity (I) or maximum ground motion. V i is buildingspecific and has to be estimated > 6
Vulnerability index estimation V i = V i* + V m 0 (low vulnerablility) < Vi < 1 (high vulnerability) > V i * : first order parameters Building material and typology >V m: Second order parameters: Geometry, position of the building with respect to other buildings, etc After RISK-UE, 2003 > 7
Typologie D esc ription Vale urs représ entatives V i m in - + m ax V i V i V i V i V i M 1 M urs po rteu rs en m aço nne rie d e pie rres c om pos é es de : M 1.1 M oello ns 0,62 0,81 0,873 0,98 1,02 M 1.2 P ierr es app areill ées 0,46 0,65 0,74 0,83 1,02 M 1.3 P ierr es de taille 0,3 0,49 0,616 0,793 0,86 M 2 A dob e 0,62 0,687 0,84 0,98 1,02 M 3 M urs po rteu rs en m aç o nne rie n on a rm é e: M 3.1 P lanc he r en b ois 0,46 0,65 0,74 0,83 1,02 M 3.2 V oûtes en m aç o nn erie 0,46 0,65 0,776 0,953 1,02 M 3.3 P lanc he rs a vec poutr elles m étalliq ues et m aç onn eri e 0,46 0,527 0,704 0,83 1,02 M 3.4 P lanc he r en b éton a rm é 0,3 0,49 0,616 0,793 0,86 M 4 M urs po rteu rs en m aç o nne rie a rm é e ou c onfi née 0,14 0,33 0,451 0,633 0,7 M 5 Cons tructions en m aç o nn eri e ren forcé es dans leur e ns em bl e 0,3 0,49 0,694 0,953 1,02 R C 1 S ys tèm e poteaux /p outres -0,02 0,047 0,442 0,8 1,02 R C 2 M urs de refen d en b éton -0,02 0,047 0,386 0,67 0,86 S ys tèm e poteaux /p outres a vec m u r de R C 3.1 rem plis s ag e en m aç on neri e no n arm ée -0,02 0,007 0,402 0,76 0,98 S truc tures ré guli ères S ys tèm e poteaux /p outres a vec m u r de rem plis s ag e en m aç on neri e no n arm ée R C 3.2 S truc tures irrég ulières (i.e. sy stèm e porteu r 0,06 0,127 0,522 0,88 1,02 irré gulie r, rem pliss ages irrég uliers, ni vea u s ouple ) R C 4 S truc ture m ix te en b éton a rm é (po rtiqu es et m urs en b éton ) -0,02 0,047 0,386 0,67 0,86 R C 5 M urs en b éton p ré fa bri qué 0,14 0,207 0,384 0,51 0,7 R C 6 S truc ture e n béto n préfab riq ué a vec m u rs de re fen d en b éton 0,3 0,367 0,544 0,67 0,86 S1 S ys tèm e poteaux /p outres en acier -0,02 0,467 0,363 0,64 0,86 S2 S truc ture e n ac ier cont re ve nté -0,02 0,467 0,287 0,48 0,7 S3 S ys tèm e poteaux /p outres en ac ier a vec m ur d e rem plis s ag e en m aç on neri e no n arm ée 0,14 0,33 0,484 0,64 0,86 S4 S ys tèm e poteaux /p outres en ac ier a vec m ur d e re fen d béto n coul és en place -0,02 0,047 0,224 0,35 0,54 S5 S ys tèm e de c om posa nt acier et bét on a rm é -0,02 0,257 0,402 0,72 1,02 W S truc ture e n bois 0,14 0,207 0,447 0,64 0,86 First order vulnerability index (V* i ) After RISK-UE, 2003 > 8
Facteurs aggravants pour les bâtiments RC et acier Irrégularité en plan Irrégularité élévation Facteurs de vulnérabilité Code Bas Code Moyen Bas (1, 2ou 3) -0,04-0,04 Nb d'étages Moyen (4, 5 ou 6) 0 0 Haut (7 ou plus) +0.08 +0.06 Forme (L, C) Oui +0.02 +0.01 0 Protubérance Oui +0.02 +0.01 0 Saillie Oui +0.02 +0.01 0 Retrait Oui +0.02 +0.01 0 Joints insuffisants (non PS) Oui +0.04 0 0 Poteaux courts Oui +0.02 +0.01 0 Règles PS Avant 1982 : L +0.16 Après 1982 : M 0 Second order vulnerability index : V m After RISK-UE, 2003 > 9
Facteurs aggravants pour les bâtiments en maçonnerie Irrégularité en plan Irrégularité élévation Interaction entre bâtiment Facteurs de vulnérabilité Etat d'entretien Nb d'étages Forme (L, C) Protubérances Saillie Retrait B = bon -0,04 M = mauvais +0.04 Bas (1 ou 2) -0,04 Moyen (3, 4 ou 5) 0 Haut (6 ou plus) +0.04 Oui +0.02 Oui +0.02 Oui +0.01 Oui +0.01 A = angle +0.04 Position dans l îlot M = milieu -0.04 T = tête d îlot +0.06 Différence de hauteur/voisin Oui +0.02 Irrégularité en toiture Oui +0.04 Décalage de plancher Oui +0.04 Transparence - démolition Oui +0.04 Balcons - cheminées Oui +0.01 Etages: haut. différente Oui +0.04 Second order vulnerability index : V m After RISK-UE, 2003 > 10
1. Use of HR optical imagery to roughly define the typology of buildings 2. Field mission to make these estimates more accurate Current methodology Issues: - Optical imagery is often not sufficient to estimate building typology - Field missions are time and cost consuming - Due to lack of time and budgets, it is difficult to estimate the vulnerability of buildings over the entire area of interest with an acceptable accuracy Can Remote Sensing help filling the vulnerability index databases? Lourdes vulnerability assessment, C. Negulescu et al., BRGM 2006 > 11
Use of remote sensing to estimatev m Unfortunately, remote sensing has more potential to retrieve second order parameters Use of ISTAR images (ortho-images (50cm res.) and digital surface model (DSM, 1m res.) (B. Poisson, BRGM 2005) Test results: Estimation number of floors: error of 1-2 floors (insufficient accuracy, due to slopes, vegetation, chimneys ) Estimation of building height differences, of building geometry, presence of chimneys, position with respect to other buildings: satisfactory results, comparable to fields surveys. > 12
Use of remote sensing to estimate V* i In the mean term, a library of spectra of urban walls should be collected in order to enable discrimination of materials using in-situ spectrometers. In the long term, airborne hyper-spectral data could be used to retrieve buildings materials Advantage: - automated method, that would reduce mission costs Drawbacks: - coatings will induce errors - difficulties to discriminate between spectrums (350-2500 nm) because the intrinsic and contextual variability within one class of materials is high (Feasability study to discriminate urban materials at 20cm spatial resolution in visible and infrared wavelength, S. Lacherade et al., ONERA, IGN (CNES support)) > 13