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International Conference on Computational Science and Its Applications

ICCSA 2017: Computational Science and Its Applications – ICCSA 2017 pp 278–293Cite as

A Geospatial Decision Support Tool for Seismic Risk Management: Florence (Italy) Case Study

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10405))

Abstract

Seismic risk assessment, which attempts to predict earthquake-induced physical impacts on structures and infrastructures, casualties and losses can be a powerful tool to support emergency response planning as well as the development of effective mitigation strategies. The Civil Protection (CP) Department of Florence Municipality commissioned this study as historical earthquakes showed an appreciable seismic risk for the city that needed careful civil protection planning. A Decision Support System DSS (CIPCast-ES) developed by ENEA, APIC Lab, in the framework of the EU-funded project CIPRNet, was used to simulate the seismic and impact scenarios for two major historical earthquakes felt in Florence, to assess the earthquake-induced damage at single building level, and the relative expected consequences on population. The possibility to account for the seismic microzonation (i.e. the possible amplification of the seismic hazard and therefore of the expected impacts due to soil conditions) was also included within DSS. The results of the scenario analysis, presented in the paper in tabular format, were provided to the CP of Florence Municipality as queryable, interactive and end-user friendly web-version maps.

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Notes

  1. 1.

    When the information on the number of stories above the ground was not available it was estimated as the height divided by 3, being 3 m the standard inter-story height for residential building in Italy.

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Acknowledgement and Disclaimer

This article was derived from the FP7 project CIPRNet, which has received funding from the European Union’s 7th Framework Programme for research, technological development and demonstration under grant agreement no. 312450. The contents of this article do not necessarily reflect the official opinion of the EU. Information and views expressed in the paper are based on personal research experiences, therefore responsibility for that lies entirely with the authors. There are several variables and data uncertainties that might affects and alter the results reported in the paper.

Author information

Authors and Affiliations

  1. Fondazione Parsec (Former Prato Ricerche), Prato, PO, Italy

    Luca Matassoni & Andrea Fiaschi

  2. University of Canterbury, Christchurch, New Zealand

    Sonia Giovinazzi

  3. ENEA Italian National Agency for New Technologies, Energy and Sustainable Economic Development, DTE-SEN-APIC Lab, Casaccia Research Centre, Rome, Italy

    Maurizio Pollino, Luigi La Porta & Vittorio Rosato

Authors
  1. Luca Matassoni
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  2. Sonia Giovinazzi
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  3. Maurizio Pollino
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  4. Andrea Fiaschi
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  5. Luigi La Porta
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  6. Vittorio Rosato
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Corresponding author

Correspondence to Sonia Giovinazzi .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Trieste, Trieste, Italy

    Giuseppe Borruso

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  9. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  10. University of Trieste, Trieste, Italy

    Alfredo Cuzzocrea

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Matassoni, L., Giovinazzi, S., Pollino, M., Fiaschi, A., La Porta, L., Rosato, V. (2017). A Geospatial Decision Support Tool for Seismic Risk Management: Florence (Italy) Case Study. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10405. Springer, Cham. https://doi.org/10.1007/978-3-319-62395-5_20

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  • DOI: https://doi.org/10.1007/978-3-319-62395-5_20

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