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

A significant part of the existing building stock in regions of low to moderate seismic haz-
ard has been designed without modern seismic considerations and is, in the meantime,
exceeding its design life span. The assessment of seismic performance poses an engineer-
ing 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, rais-
ing 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, representa-
tive 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 typo-
logical 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.