Reducing uncertainty in seismic assessment of multiple masonry buildings based on monitored demolitions

Abstract A significant part of the existing building stock in regions low to moderate seismic hazard has been designed without modern considerations and is, meantime, exceeding its design life span. The assessment performance poses an engineering challenge, due unknown material properties, undocumented structural interventions scarcity event-based information. Operational modal analysis applied some cases verify model assumptions beyond visual inspection. However, masonry buildings exhibit amplitude-dependent stiffness even at very response amplitudes, raising questions about validity such methods. Planned demolitions provide engineers with opportunity leverage higher-amplitude vibrations generated during demolition activities better understand dynamic behaviour buildings. This paper introduces a Bayesian model-updating framework, which aims reducing uncertainty analysis, by fusing measurements best-practice models. proposed hybrid framework is nine real buildings, representative residential as typically encountered Switzerland, that have monitored controlled demolition. vast reduction prediction achieved through data-driven updating, additionally exposing intra- inter-typological differences terms capacity ductility. In addition, between updated predictions typical generic typological curves are discussed. Overall, this contribution demonstrates, applies discusses practical benefits straightforward methodology for monitoring data into evaluation structures.