The production of fragility functions describing the probable behaviour and damage on historical buildings is a key step in a method for the estimation of the magnitude of historical seismic events that uses a Bayes'. The fragilities are estimated by integrating the structural capacity with the seismic demand using either static methods, as the Capacity Spectrum Method (CSM), or dynamic methods, as Incremental Dynamic (IDA) and Multiple Stripes Analysis (MSA). Uncertainties in both resistance, demand, and distance and magnitude models propagate to the posterior magnitude distribution. The present paper studies the effect of uncertainties related both to the production of fragility functions and prior distributions, in the estimation of the magnitude of the 1763 Komárom earthquake (in historical Hungary). In the XVIII century most of the structures in the region were built of earth, adobe, clay or stone masonry, which is complex to model. While micro or detailed macro-modelling strategies are computationally costly, simplified macro-approaches are often more efficient, but require a pre-identification of the failure mode(s) and the determination of the backbone curve. For this study, a simplified macro-model of a Hungarian peasant house archetype is calibrated for CSM and IDA. The physical and geometrical uncertainties are incorporated in the fragilities using Monte-Carlo simulation. Prior magnitude and distance distributions are studied. The final magnitude estimates are presented and discussed.
Reconstructing Point Sets from Distance Distributions
