ANIDIS - L'ingegneria Sismica in Italia, ANIDIS XX - 2025

Dimensione del carattere:  Piccola  Media  Grande

ANALYSIS OF OPEN DATA RESOURCES FOR MULTI-SCALE PROBABILISTIC RISK ASSESSMENT OF BUILT ENVIRONMENT

Antonio Sandoli, Tony Fierro, Filippo Santucci de Magistris, Francesca Di Virgilio, Fabbrocino Giovanni

Ultima modifica: 2025-07-25

Sommario


The availability and consistency of technical and seismological data play a crucial role in multi-scale seismic risk assessment of the built environment, typically based on the use of fragility functions. Nevertheless, in a general framework of risk assessment, multi-data can be also exploited for other purposes related to risk quantification from a global standpoint: active prevention policies, rational management of emergency as well as an empowerment and refinement of reconstruction processes to increase the resilience of the communities affected by significant earthquakes.

To this aim, assessing the availability of consistent databases is crucial to achieve a homogeneous level of knowledge of the territory and to perform reliable and comparable multi-scale analyses of the existing built environment.

In this paper, the architectures of the main existing Italian databases (e.g., IRMA, CARTIS, ISTAT, Da.D.O., National Dataset provided by DPC, etc.) have been critically analyzed with the purpose of identifying the homogeneity level of data collected (in the different databases) and their suitability in the perspective of multi-scale risk applications. It has been evaluated the versatility of the data to be used for probabilistic structural safety evaluations, as well as the correctness and validity of exposure and geotechnical data.

The achieved results showed that the analyzed databases contain significant and often complementary data that fit the requirements for probabilistic risk assessment primarily at a large to medium scale of analysis. They are also heterogeneous and point out the need of interoperability procedures able allow an exchange, integration of missed information for the development of more complete and consistent data collections for risk analysis.


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