The role of the urban system dysfunction in the assessment of seismic risk in the Mt. Etna area (Italy)

A procedure for seismic risk assessment is applied to the Mt. Etna area (eastern Sicily, Italy) through assessment of urban system dysfunction following the occurrence of an earthquake. The tool used is based on the Disruption Index as a concept implemented in Simulator QuakeIST, which defines urban disruption following a natural disaster. The first element of the procedure is the definition of the seismic input, which is based on information about historical seismicity and seismogenic faults. The second element is computation of seismic impact on the building stock and infrastructure in the area considered. Information on urban-scale vulnerability was collected and a geographic information system was used to organise the data relating to buildings and network systems (e.g., building stock, schools, strategic structures, lifelines). The central idea underlying the definition of the Disruption Index is identification and evaluation of the impact on a target community through the physical elements that most contribute to severe disruption. The procedure applied in this study (i.e., software and data) constitutes a very useful operational tool to drive the development of strategies to minimise risks from earthquakes.

[1]  Carlos Sousa Oliveira,et al.  QuakeIST® earthquake scenario simulator using interdependencies , 2016, Bulletin of Earthquake Engineering.

[2]  Simona Scollo,et al.  Tephra hazard assessment at Mt. Etna (Italy) , 2013 .

[3]  F. Mota de Sá,et al.  Disruption index, DI: an approach for assessing seismic risk in urban systems (theoretical aspects) , 2014, Bulletin of Earthquake Engineering.

[4]  Sonia Giovinazzi,et al.  The vulnerability assessment of current buildings by a macroseismic approach derived from the EMS-98 scale , 2007 .

[5]  B. Behncke,et al.  Lava flow hazard at Mount Etna (Italy): New data from a GIS-based study , 2005 .

[6]  M. L. Sousa,et al.  Building vulnerability and seismic risk analysis in the urban area of Mt. Etna volcano (Italy) , 2016, Bulletin of Earthquake Engineering.

[7]  Carlos Sousa Oliveira,et al.  The concept of a disruption index: application to the overall impact of the July 9, 1998 Faial earthquake (Azores islands) , 2011, Bulletin of Earthquake Engineering.

[8]  V. Petrini,et al.  Censimento di vulnerabilità degli edifici pubblici, strategici e speciali nelle regioni Abruzzo, Basilicata, Calabria, Campania, Molise, Puglia e Sicilia – Cap. 4: Risultati del Progetto , 1999 .

[9]  G. Zonno,et al.  Guidelines to use the software PROSCEN , 2010 .

[10]  R. Rotondi,et al.  Mining Macroseismic Fields to Estimate the Probability Distribution of the Intensity at Site , 2009 .

[11]  Carlos Sousa Oliveira,et al.  The Disruption Index (DI) as a tool to measure disaster mitigation strategies , 2015, Bulletin of Earthquake Engineering.

[12]  Massimiliano Stucchi,et al.  CPTI11, the 2011 version of the Parametric Catalogue of Italian Earthquakes , 2011 .

[13]  Ragnar Sigbjörnsson,et al.  Urban Disaster-Prevention Strategies Using Macroseismic Fields and Fault Sources , 2012 .

[14]  R. Rotondi,et al.  Probabilistic modelling of macroseismic attenuation and forecast of damage scenarios , 2016, Bulletin of Earthquake Engineering.

[15]  Almantas Kakareka,et al.  What is Vulnerability Assessment , 2013 .

[16]  M. Coltelli,et al.  Monitoring and forecasting Etna volcanic plumes , 2009 .

[17]  M. S. Barbano,et al.  Characterization of seismicity at Mt. Etna volcano (Italy) by inter-event time distribution , 2014 .

[19]  D. Andronico,et al.  A multidisciplinary approach to detect active pathways for magma migration and eruption at Mt. Etna (Sicily, Italy) before the 2001 and 2002–2003 eruptions , 2004 .

[20]  M. van der Borst,et al.  An overview of PSA importance measures , 2001, Reliab. Eng. Syst. Saf..

[21]  R. Rotondi,et al.  Probabilistic analysis of macroseismic fields : Iceland case study , 2012 .

[22]  R. Rotondi,et al.  Forecasting seismic scenarios on Etna volcano (Italy) through probabilistic intensity attenuation models: A Bayesian approach , 2013 .

[23]  R. Azzaro.  Seismicity and Active Tectonics in the Etna Region: Constraints for a Seismotectonic Model , 2013 .

[24]  R. Azzaro,et al.  Estimating the Magnitude of Historical Earthquakes from Macroseismic Intensity Data: New Relationships for the Volcanic Region of Mount Etna (Italy) , 2011 .

[25]  Carlos Sousa Oliveira,et al.  Earthquake Risk Reduction: From Scenario Simulators Including Systemic Interdependency to Impact Indicators , 2014 .

[26]  R. Rotondi,et al.  Analysis of macroseismic fields using statistical data depth functions: considerations leading to attenuation probabilistic modelling , 2016, Bulletin of Earthquake Engineering.

[27]  S. Lagomarsino,et al.  Macroseismic and mechanical models for the vulnerability and damage assessment of current buildings , 2006 .

[28]  Salvatore D'Amico,et al.  Probabilistic seismic hazard at Mt. Etna (Italy): The contribution of local fault activity in mid-term assessment , 2013 .

[29]  C. Negro,et al.  Lava flow hazards at Mount Etna: constraints imposed by eruptive history and numerical simulations , 2013, Scientific Reports.

[30]  F. Greco,et al.  Intrusive mechanism of the 2002 NE‐Rift eruption at Mt. Etna (Italy) inferred through continuous microgravity data and volcanological evidences , 2003 .

[31]  R. Azzaro,et al.  Seismic hazard assessment in the volcanic region of Mt. Etna (Italy): a probabilistic approach based on macroseismic data applied to volcano-tectonic seismicity , 2016, Bulletin of Earthquake Engineering.

[32]  Rui Pinho,et al.  A comparison of seismic risk maps for Italy , 2009 .