GNGTS 2018 - 37° Convegno Nazionale

GNGTS 2018 S essione 2.2 397 Bohm G., Luzi L., Galadini F.; 2011: Tomographic depth seismic velocity model below the plain of Norcia (Italy) for site efect studies. Boll. Geof. Teor. Appl., 52(2), 197-209, doi: 10.4430/bgta0002. Chiaraluce L., Di Stefano R., Tinti E., Scognamiglio L., Michele M., Casarotti E., Cattaneo M., De Gori P., Chiarabba C., Monachesi G., Lombardi A., Valoroso L., Latorre D., Marzorati S.; 2017: The 2016 Central Italy Seismic Sequence: A First Look at the Mainshocks, Aftershocks, and Source Models . Seismol. Res. Lett., 88, 757–771, doi: 10.1785/0220160221. Di Giulio G., Rovelli A., Cara F., Azzara R. M., Marra F., Basili R., and CasertaA.; 2003: Long-duration asynchronous ground motions in the Colfiorito plain, central Italy, observed on a two-dimensional dense array. J Geophys. Res., 108, B10, 2486, doi: 10.1029/2002jb002367. Di Giulio G., de Nardis R., Boncio P., Milana G., Rosatelli G., Stoppa F. and Lavecchia G.; 2016: Seismic response of a deep continental basin including velocity inversion: the Sulmona intramontane basin (Central Apennines, Italy). Geoph. Journ. Int., 204, 418-439, doi: 10.1093/gji/ggv444. Galli P., Castenetto S., &Peronace E.; 2017: The macroseismic intensity distribution of the 30 October 2016 earthquake in central Italy (Mw 6.6): Seismotectonic implications. Tectonics, 36, 2179–2191. doi: 10.1002/2017TC004583. Luzi L., D’Amico M., Massa M., Puglia R.; 2018: Site effects observed in the Norcia intermountain basin (Central Italy) exploiting a 20‑year monitoring. Bull. Earthquake Eng., doi: 10.1007/s10518-018-0444-3. Porreca M., Minelli G., Ercoli M., Brobia A., Mancinelli P., Cruciani F., Giorgetti C., Carboni C., Mirabella F., Cavinato G., Cannata A., Pauselli C., Barchi M.R.; 2018: Seismic reflection profiles and subsurface geology of the area interested by the 2016–2017 earthquake sequence (Central Italy). Tectonics, 37, 1-22, doi: 10.1002/2017TC004915 SYNTHETIC ACCELEROGRAMS FOR HAZARD EVALUATION AND RESPONSE-HISTORY ANALYSIS OF BUILDINGS M. Fasan, M. Barnaba Department of Engineering and Architecture, University of Trieste, Italy Introduction. Non-linear Time History Analysis (NLTHA) of structures is the most sophisticated tool used to understand the real dynamic behaviour of structures (FIB, 2012). The goodness of results relies on an accurate definition of the materials properties, their hysteretic behaviour and the geometry of the structure to be examined, as well as on the definition of the dynamic excitations represented by acceleration time histories. These accelerograms must represent, on average, the hazard of the site under examination, commonly represented by an acceleration response spectrum. Usually the target response spectrum is defined, in a Probabilistic (PSHA) or Deterministic (DSHA) Seismic Hazard Assessment, through Ground Motion Prediction Equations (GMPEs). Therefore, ground motions should have magnitude, source distance and focal mechanism consistent with the sources that control the hazard at the site of interest. Moreover, site soil conditions and the possibility of experiencing near fault effects such as directivity and fling-step needs to be considered (NIST, 2011). Usually, acceleration time histories are selected from databases of records (e.g. the European Strong Motion (ESM) database (Luzi et al. , 2016) in order to satisfy all the above-mentioned characteristics and to match, over a defined range of periods, the target response spectrum. As the tolerance on the variability of the selection parameters becomes stronger, the lack of data becomes evident and some modifications (e.g. linear scaling) of the original recorded ground motions are needed if an adequate number of ground motion is to be used. A source of time histories could be the generation of artificial accelerograms (Gasparini and Vanmarke, 1976) or the use of the “response spectrum matching” technique (Al Atik and Abrahamson, 2010; Grant and Diaferia, 2013). However, these techniques have no physical meaning and there are concerns that their use could lead to biased results (Bazzurro and Luco, 2006; Iervolino et al. , 2010).