Widespread urbanization is a recent phenomenon. Cities are centers of economic activity and innovation so that many people are attracted to better jobs positions, services and prospects for improved living conditions. Furthermore, the urban population growth varies considerably worldwide but a common increasing trend is observed in the large cities. Then, modern society is critically dependent on a network of complex and interdependent systems which compose the urban environment. For this reason, urban areas are particularly vulnerable due to the high concentration of people and economic assets, and in many cases, their hazard-prone location. When a natural disaster such as earthquake strikes an urban area, the majority of the losses in terms of casualties and repair costs are due to the buildings extensive damage and collapse. Therefore, urban buildings portfolio represents the most vulnerable physical system of a built environment. This thesis presents research conducted in the prediction of the damage experienced by the building stock located in urban areas following a seismic event. A physical simulation model is proposed to assess the seismic capacity of individual building and then estimate the level of damage caused by a pre-defined seismic scenario on the exposed building portfolio. A large scale virtual city, named IDEAL CITY, consisting of different buildings categories and infrastructure is designed envisioned as being representative of the typical Italian building stock. An intensive data collection and processing is performed to create a comprehensive building exposure database that provides numerous benefits in estimates of potential damage due to catastrophic events. The proposed simulation model provides an efficient perspective to estimate the seismic vulnerability of any individual building within a large-scale area subjected to a given seismic scenario.

Large scale simulation of IDEAL CITY under seismic scenario / Marasco, Sebastiano. - (2018 Sep 19).

Large scale simulation of IDEAL CITY under seismic scenario

MARASCO, SEBASTIANO
2018

Abstract

Widespread urbanization is a recent phenomenon. Cities are centers of economic activity and innovation so that many people are attracted to better jobs positions, services and prospects for improved living conditions. Furthermore, the urban population growth varies considerably worldwide but a common increasing trend is observed in the large cities. Then, modern society is critically dependent on a network of complex and interdependent systems which compose the urban environment. For this reason, urban areas are particularly vulnerable due to the high concentration of people and economic assets, and in many cases, their hazard-prone location. When a natural disaster such as earthquake strikes an urban area, the majority of the losses in terms of casualties and repair costs are due to the buildings extensive damage and collapse. Therefore, urban buildings portfolio represents the most vulnerable physical system of a built environment. This thesis presents research conducted in the prediction of the damage experienced by the building stock located in urban areas following a seismic event. A physical simulation model is proposed to assess the seismic capacity of individual building and then estimate the level of damage caused by a pre-defined seismic scenario on the exposed building portfolio. A large scale virtual city, named IDEAL CITY, consisting of different buildings categories and infrastructure is designed envisioned as being representative of the typical Italian building stock. An intensive data collection and processing is performed to create a comprehensive building exposure database that provides numerous benefits in estimates of potential damage due to catastrophic events. The proposed simulation model provides an efficient perspective to estimate the seismic vulnerability of any individual building within a large-scale area subjected to a given seismic scenario.
19-set-2018
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Descrizione: Doctoral Thesis
Tipologia: Tesi di dottorato
Licenza: Creative commons
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2714786
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