Towards a multi-parametric fragility model for Italian masonry buildings based on the informative level

Abstract

This study provides a multi-parametric fragility model for Italian masonry buildings, which varies based on available information about buildings characteristics, sources from data of varying accuracy level. Three increasing levels are considered: limited, extensive, and exhaustive. These levels correspond to different degree of knowledge about the building stock, ranging from detailed in situ inspections, where a comprehensive understanding of nearly all the building features is achievable, to the case where it must necessarily rely upon a limited data (census data). This multilevel classification is established based on the number of explanatory parameters employed in defining building taxonomies. The parameters considered in this study are derived from post-earthquake damage data collected after the 2009 L'Aquila earthquake. They have undergone a thorough review and a refinement process and have been supplemented with 2001 census data to overcome the critical issue of data completeness. In addition to providing information about the geometric and typological characteristics of residential masonry buildings, the same data source also yields insights into the extent of damage sustained by these buildings. This, in turn, enables the derivation of a Damage Probability Matrix (DPM), obtained as a function on ground motion intensity values evaluated from ShakeMap of the event. The outcomes of a nonparametric statistical test, applied on DPMs, are used to drive an agglomerative clustering procedure, to establishing a hierarchical ranking of the parameters in term of their discriminatory power. The absolute (number of cases where the null testing hypothesis is rejected) and the relative (examining the test’s strength) comparison of the outcomes, allows to identify the parameters with the greatest discriminatory power. These are the one that define the branches of the taxonomies under consideration. Conversely, the less influential parameters with regard to seismic vulnerability are eligible to be grouped together, allowing the definition of a more compact taxonomy. This approach yields distinct sets of fragility curves, contingent upon the information level gained for the specific building stock being investigated, ranging from census data to comprehensive in-situ surveys. A last focus on the impact of the enhancement in the level of knowledge on seismic vulnerability is also done.