Seismic Vulnerability Assessment Of “Sion Cathedral” (Switzerland): An Integrated Approach To Detect And Evaluate Local Collapse Mechanisms In Heritage Buildings
Seismic assessment of existing heritage buildings remains a challenging task. There is a high level of complexity and uncertainty compared with the assessment of standard buildings. Heritage masonry churches are usually prone to partial collapses during earthquake due to local loss of stability, and exhibit particular seismic vulnerabilities. An important step in the seismic analysis of heritage masonry buildings is the detection of local mechanisms. The Italian Building Code provides a simplified approach (LV1-churches) to assess the vulnerability of heritage churches evaluating and comparing 28 potential mechanisms. A general index of vulnerability and hierarchy between mechanisms is thereby provided. Verification of safety against local mechanisms can also be carried out using the kinematic approach. This procedure is based on evaluating the horizontal action needed to activate out-of-plane collapse mechanisms. Based on a full-scale study (Sion Cathedral), this paper evaluates the reliability of the “LV1-church” approach and of the kinematic approach through a comparison with the results obtained with a complex 3D model using the Applied Element Method.
A large number of buildings in regions with low to medium seismic hazard have been designed without considering earthquake actions. Retrofitting of all buildings that fail to meet modern code requirements is economically, technically and environmentally unsustainable. Decision-making regarding retrofitting necessity and prioritization is complex. Ambient vibrations are non-destructive and easy to measure, and thus an attractive data source. However, ambient vibrations have very low amplitudes, which potentially lead to sensitivity to testing conditions and stiffness contributions from non-structural elements. Seismic assessment necessitates non-linear behavior extrapolation from linear measurements, which results in biased model predictions.