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Integration of Field Surveys and Remote Sensing Techniques for Seismic Vulnerability Assessment: Methods and Applications to the Val d’Agri Area (Southern Italy)

Liuzzi, Mariangela (2018) Integration of Field Surveys and Remote Sensing Techniques for Seismic Vulnerability Assessment: Methods and Applications to the Val d’Agri Area (Southern Italy). Dissertation, University of Basilicata.

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Abstract

Earthquake damage scenarios addressing earthquake risk management and planning of mitigation strategies in urban areas rely on the vulnerability assessment of the built environment. Building inventories, involving physical parameters affecting the buildings seismic vulnerability, are crucial to perform this assessment. However, the data collection is challenging and demanding in terms of time, human and economic resources. This issue is being jointly tackled by the Civil Engineering and the Remote Sensing communities: in this framework, this Ph.D. thesis aims to support rapid vulnerability assessments by investigating the existing Damage Estimation Models (DEMs) and developing a Remote Sensing approach providing useful data for such DEMs. Earthquake damage scenarios have been calculated for four villages in the Val d’Agri area (Southern Italy) using a large in situ database. Moreover, this database supported the development of a Remote Sensing transferable approach aimed to contribute to the creation of building inventories where detailed in situ data are missing. Specifically, the calculation of damage scenarios has taken into account the experiences and studies carried out in the Earthquake Engineering field in the School of Engineering of Basilicata University: among them, seismic vulnerability assessments and damage scenarios for Potenza town (Dolce et al., 2003, 2006; Chiauzzi et al., 2012), analytical methods for the quantification of damage on reinforced concrete buildings (Masi et al., 2009, 2015), vulnerability assessment of Val d’Agri area (Masi et al., 2006, 2014). Val d’Agri is characterized by high seismic risk, and plays a strategic economic role due to the presence of oil extraction plants. This work aims to further develop the study of Masi et al. (2014), based on a detailed building-by-building database about almost 18000 buildings, collected in a pre-event technical survey in the period 2001-2006. Such in situ database, as well as the cited methods and approaches have been used in this work to quantify in four villages the expected consequences on the building stock of two earthquakes having EMS- 98 VII and VIII grade Intensity, the latter taking into account site effects. The results of the scenarios in terms of distribution of buildings in damage levels and number of unusable buildings are presented and compared. These indicate the more suitable models for describing the seismic behavior of the analyzed building types and to quantify the expected losses due to the considered earthquake. In particular, it is shown that limited losses can be expected from the reinforced concrete building stock, usually responsible for more severe impacts with respect to the masonry buildings due to its generally larger volume.

Item URL in elib:https://elib.dlr.de/125755/
Document Type:Thesis (Dissertation)
Title:Integration of Field Surveys and Remote Sensing Techniques for Seismic Vulnerability Assessment: Methods and Applications to the Val d’Agri Area (Southern Italy)
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Liuzzi, MariangelaUNSPECIFIEDUNSPECIFIED
Date:March 2018
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Number of Pages:159
Status:Published
Keywords:Urban remote sensing, risk assessment, vulnerability, machine learning
Institution:University of Basilicata
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Remote sensing and geoscience
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Geo Risks and Civil Security
Deposited By: Taubenböck, Dr. Hannes
Deposited On:09 Jan 2019 20:45
Last Modified:09 Jan 2019 20:45

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