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CARTIS: a method for the typological-structural characterization of Italian ordinary buildings in urban areas

Author(s):




Medium: journal article
Language(s): English
Published in: Frontiers in Built Environment, , v. 9
DOI: 10.3389/fbuil.2023.1129176
Abstract:

Large-scale risk assessments relevant to natural hazards are commonly based on very poor exposure and vulnerability data, often drawn from census data. In fact, obtaining a detailed knowledge of the built heritage is a very hard task especially for those countries, like Italy, characterized by very high urban density and large variety of building typologies, where a building-by-building knowledge can sound as a utopian ambition. Nevertheless, exposure and vulnerability are two of the four factors governing, along with hazard and capacity, risk convolution, and hence their uncertainties yield to corresponding uncertainties in the resulting expected losses. The lack of suitable information on building typologies is responsible of very strong simplifications in risk analyses, like the assumption of the same building typologies, indistinctly scattered all over the Country territory, without distinctions at a local or at a regional level. With the goal of improving exposure description and reducing such uncertainties, since 2014 the Italian Civil Protection Department (ICPD) has undertaken a new research branch in the framework of ReLUIS (Network of University Laboratories in Earthquake Engineering) projects, dedicated to territorial analyses, by funding also the CARTIS project. The project has the goal to characterize the building structural typologies trough a data collection at a local and an extensive scale in Italy, with the final aim to improve the reliability of seismic risk analyses. The paper describes the method and some first statistics so far elaborated.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.3389/fbuil.2023.1129176.
  • About this
    data sheet
  • Reference-ID
    10732241
  • Published on:
    13/06/2023
  • Last updated on:
    13/06/2023
 
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