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

Abstract

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

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