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A simplified model based on self-organized criticality framework for the seismic assessment of urban areas

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 نشر من قبل Andrea Rapisarda
 تاريخ النشر 2017
  مجال البحث فيزياء
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The analysis of the seismic vulnerability of urban centres has received a great attention in the last century. In order to estimate the seismic vulnerability of a densely populated urban area, it would in principle be necessary to develop in-depth analyses for predicting the dynamic behaviour of the individual buildings and their structural aggregation. Such analyses, however, are extremely cost-intensive, require great processing time and above all expertise judgement. It is therefore very useful to define simplified rules for estimating the seismic vulnerability of whole urban areas. In the last decades, the Self-Organized Criticality (SOC) scenario has gained increasing credibility as a mathematical framework for explaining a large number of naturally occurring extreme events, from avalanches to earthquakes dynamics, from bubbles and crises in financial markets to the extinction of species in the evolution or the behaviour of human brain activity. All these examples show the intrinsic tendency common to many phenomena to spontaneously organize into a dynamical critical state, whose signature is the presence of a power law behaviour in the frequency distribution of events. In this context, the Olami-Feder- Christensen (OFC) model, introduced in 1992, has played a key role in modelling earthquakes phenomenology. The aim of the present paper is proposing an agent-based model of earthquake dynamics, based on the OFC self- organized criticality framework, in order to evaluate the effects of a critical sequence of seismic events on a given large urban area during a given interval of time. The further integration of a GIS database within a software environment for agent-based simulations, will allow to perform a preliminary parametric study of these effects on real datasets. The model could be useful for defining planning strategies for seismic risk reduction



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