Sources of errors and uncertainties in large-scale seismic vulnerability assessment methods

Abstract : Assessing the seismic vulnerability of urban areas is essential to prevent potential damage to the exposed assets and assist local administrations in the prioritization of cost-effective risk mitigation measures in order to enhance urban resilience against future earthquakes and reduce the number of casualties and economic losses. To this end, large-scale empirical methods offer great advantages, allowing to estimate the vulnerability level of extended areas without resorting to sophisticated analyses that are not feasible but at the building scale. However, these assessments are inherently subject to several sources of errors and uncertainties that can significantly influence their outcomes and must be controlled. By comparing two index-based seismic vulnerability assessment methods well known in the literature, this paper intends to dissect this aspect with particular reference to three parameters, i.e. seismic intensity, ductility factor and vulnerability factor. The implications associated with vulnerability models calibrated through data collected from different historic centres are also discussed. The urban area under investigation is the municipality of Campotosto in the province of L'Aquila (Abruzzo, Italy), a case study of great relevance given its proximity to the fault associated with the last seismic event that hit Central Italy in January 2017. The study aims to provide a comprehensive understanding of the challenges involved in seismic vulnerability assessments, advocating for continuous refinement of methodologies to enhance the predictive capabilities of these critical assessments in the face of increasing seismic risk. • Large-scale vulnerability evaluations are subject to sources of error and uncertainties. • Two vulnerability index-based methods are applied to the same case study to identify these sources. • The influence of three parameters, i.e. seismic intensity, ductility factor and vulnerability factor, is analysed and quantified. • Large-scale vulnerability models need to account for these uncertainties to produce reliable results.