Amplitude-dependent model updating of masonry buildings undergoing demolition

Abstract

Precise knowledge of dynamic characteristics and data-driven inference of material properties of existing buildings are key for assessing their seismic capacity. While dynamic measurements on existing buildings are typically extracted under ambient conditions, masonry, in particular, exhibits nonlinear behavior at already very low shaking amplitudes. This implies that material properties, inferred via data-driven model updating under ambient conditions, may be inappropriate for predicting behavior under seismic actions. In addition, the relative amount of nonlinearity arising from structural behavior and soilstructure interaction are often unknown. In this work, Bayesian model updating is carried out on field measurements that are representative of increasing levels of shaking, as induced during demolition, on a pre-code masonry building. The results demonstrate that masonry buildings exhibit nonlinear behavior as the elastic modulus drops by up to 18% in the so-called equivalent elastic range, in which the observed frequency drop is reversible, prior to any visible sign of damage. The impact of this effect on the seismic assessment of existing structures is investigated via a nonlinear seismic analysis of the examined case study, calibrated under dynamic recordings of varying response amplitude. While limited to a single building, such changes in the inferred material properties results in a significant reduction of the safety factor, in this case by 14%.