GNGTS 2016 - Atti del 35° Convegno Nazionale

424 GNGTS 2016 S essione 2.3 in Y direction (transversal direction of the building) showing a good level of prediction in terms of kinematic behaviour. The edge of the building monitored by channels 9, 18 and 13 shows the highest inter-story drift ratio for both experimental data (in green) and estimated values (in red): the maximum experimental drift is located between Ch. 9 and Ch.2, whereas the maximum drift predicted by SMAV is positioned on the same side of the building but between Ch. 18 and Ch. 9, at the upper floor. The values are also comparable (2.25‰ and 2.13‰ respectively), appearing consistent with the light damage encountered in that area of the building. It is worth pointing out that two sources of error are also implied both in the experimental data and in the SMAVmodel. First of all the experimental drift are not directly measured but they are obtained through a double numerical integration of the acceleration time-history, introducing a numeric error (Skolnik et al. , 2008). In addition some further uncertainty is caused by the position of external ground channels, that are not aligned with the sensors at the first floor. As concerns the SMAV model, a further approximation is introduced by some simplified assumptions: the structural damping considered for the analysis (conventionally equal to 5%) and the frequency drop estimated through the probabilistic curve are assumed to be the same for all the vibrational modes. As a consequence an error is introduced both in the evaluation of each mode response (a damping of 5% is a conventional value that doesn’t necessarily reflect the real damping of the structure during the earthquake) as well as in their combination. Conclusions. In the current study an application of the SMAV methodology to a real R.C. structure (Norcia Primary school) has been illustrated, highlighting the good ability of the SMAV model to reproduce the experimental recorded data using the earthquake of August 24, 2016, 1:36:32 UTC as seismic input. During that seismic event the structure, indeed, has remained in an operational condition and the building has only encountered a light damage in the infill panel, that is compatible with the damage level predicted by the SMAV model on the basis of the calculated inter-story drift. The maximum SMAV estimated drift value (2.13‰) in that area of the building is lower than the structural operational threshold of drift (3.00‰), supporting SMAV ability to evaluate the serviceability condition of existing buildings and the inter-story drift ratio as a good reference parameter. The differences with the experimental values are contained and they are compatible with the simplified assumptions of the adopted kinematic model and with the possible error in the experimental data. References Acunzo G., Fiorini N., Mori F., Spina D.; 2016: Modal mass estimation from ambient vibrations measurement: a method for civil buildings. Mechanical Systems and Signal Processing, in press. Acunzo G., Fiorini N., Mori F., Spina D.; 2015: VaSCO-smav: il software sviluppato per l’applicazione della metodologia SMAV (Seismic Model from Ambient Vibrations). Proceedings of the XVI Congresso Nazionale “L’ingegneria Sismica in Italia”(ANIDIS 2015). Fiorini N.,Acunzo G., Mori F., Spina D.; 2015: La metodologia SMAV per gli edifici di interesse storico-architettonico: la biblioteca dell’Abbazia di Casamari, il Palazzo delle Laudi di Sansepolcro e il palazzo comunale di Recanati. Proceedings of the XVI Congresso Nazionale “L’ingegneria Sismica in Italia”(ANIDIS 2015). Ljung L.; 1987: System Identification: Theory for the user. Prentice Hall PTR, Upper Saddle River, New Jersey, 71- 81. Mori F., Acunzo G., Fiorini N., Pagliaroli A., Spina D., Dolce M.; 2015: La metodologia SMAV (Seismic Model from Ambient Vibrations) per la valutazione dell’operatività strutturale degli edifici esistenti. Proceedings of the XVI Congresso Nazionale “L’ingegneria Sismica in Italia”(ANIDIS 2015). Peeters B. and De Roeck G.; 2001: Stochastic System Identification for Operational Modal Analysis: A Review. Journal of Dynamic Systems, Measurement and Control 123, 659-667. Skolnik D.A., KaiserW.J., Wallace J.W.; 2008: Instrumentation for Structural HealthMonitoring: Measuring Interstory Drift. The 14 th World Conference on Earthquake Engineering, October 12-17, Beijing, China. Spina D., Lamonaca B.G., Nicoletti M., DolceM.; 2011:Structural monitoring by Italian Department of Civil Protection and the case of 2009 Abruzzo seismic sequence, Bulletin of Earthquake Engineering, Vol. 9, 325-343.