GNGTS 2013 - Atti del 32° Convegno Nazionale

Bibliografia Azzaro R., Barbano M.S., D’Amico S., Tuvè T., Albarello D. and D’Amico V.; 2008: First studies of probabilistic seismic hazard assessment in the volcanic region of Mt. Etna (Southern Italy) by means of macroseismic intensities. Bollettino di Geofisica Teorica e Applicata, 49 (1), 77-91. Azzaro R., Branca S., Gwinner K. and Coltelli M.; 2012: The volcano-tectonic map of Etna volcano, 1:100.000 scale: an integrated approach based on a morphotectonic analysis from high-resolution DEM constrained by geologic, active faulting and seismotectonic data. Italian Journal of Geosciences, 131 (1), 153-170. Azzaro R., D’Amico S., Peruzza L. and Tuvè T.; 2012: Earthquakes and faults at Mt. Etna (Southern Italy): problems and perspectives for a time-dependent probabilistic seismic hazard assessment in a volcanic region. Bollettino Geofisica Teorica e Applicata, 53 (1), 75-88. Azzaro R., D’Amico S., Peruzza L. and Tuvè T; 2013: Probabilistic seismic hazard at Mt. Etna (Italy): The contribution of local fault activity in mid-term assessment. Journal of Volcanology and Geothermal Research, 251, 158-169. Bindi D., Pacor F., Luzi L., Puglia R., Massa M., Ameri G. and Paolucci R.; 2011: Ground motion prediction equations derived from the Italian strong motion database. Bulletin of Earthquake Engineering, 9 (6), 1899-1920. CMTE Working Group; 2008: Catalogo Macrosismico dei Terremoti Etnei dal 1832 al 2008. INGV, Catania, http://www. ct.ingv.it/ufs/macro/. Gresta S. and Langer H.; 2002: Assessment of seismic potential in southeastern Sicily. In: Brebbia C.A. (ed), Risk Analysis III, WITpress Southampton, Boston, 617-626. Grünthal G.; 1998: EuropeanMacroseismic Scale 1998 (EMS-98). European Seismological Commission, subcommission on Engineering Seismology, working Group Macroseismic Scales. Conseil de l’Europe. Cahiers du Centre Européen de Géodynamique et de Séismologie 15, 99 Luxembourg, http://www.ecgs.lu/cahiers-bleus/. Gruppo Analisi Dati Sismici; 2013: Catalogo dei terremoti della Sicilia Orientale – Calabria Meridionale 1999-2013. INGV, Catania, http://www.ct.ingv.it/ufs/analisti/catalogolist.php. Matthews M.V., Ellsworth W.L. and Reasenberg P.A.; 2002: A Brownian model for recurrent earthquakes. Bulletin of the Seismological Society of America, 92, 2233–2250. Zöller G., Hainzl S. and Holschneider M.; 2008: Recurrent large earthquakes in a fault region: what can be inferred from small and intermediate events?. Bulletin of the Seismological Society of America, 98 (6), 2641–2651. Seismic vulnerability assessment at urban scale based on field survey, remote sensing and census data C. Del Gaudio, P. Ricci, G.M. Verderame, G. Manfredi Department of Structural Engineering, University of Naples Federico II, Italy Introduction. In this study, a seismic vulnerability assessment at urban scale is carried out in the high-seismic city of Avellino (Southern Italy) using building stock data from different sources, and results are compared within a multilevel approach. Data on building stock characteristics were collected within the SIMURAI (2010) research project (Strumenti Integrati per il Multi Risk Assessment territoriale in ambienti urbani antropizzatI , Integrated tools for large scale multi-risk assessment in urban anthropic environment). Different sources of information are available, namely (in a growing order of accuracy): (i) census data (ISTAT, 2001) providing information on buildings aggregate for relatively large spatial units (census cells); (ii) data from an airborne Remote Sensing mission carried out over the Municipality, providing a detailed estimate of 3D geometric parameters of buildings; (iii) data from a field survey, provided detailed information on geometrical and structural characteristics of each single building. Such data are used, within a multilevel approach, in order to evaluate the influence of the detail level of input data on seismic vulnerability assessment at urban scale. To this aim, data from field survey are assumed as a reference, and when using census or Remote Sensing data, due to the lack of information affecting such data sources, some of the input parameters to the seismic vulnerability assessment procedure are assumed as random variables. Although some uncertainties unavoidably affect a seismic vulnerability assessment procedure, independent of the accuracy in data collection on building stock characteristics (e.g., epistemic uncertainty in capacity models of structural elements or aleatory uncertainty in seismic demand estimation), the use of data with a lower knowledge level leads to additional uncertainty. 50 GNGTS 2013 S essione 2.1