Characterization of URM buildings and evaluation of damages in a historical center for the seismic risk mitigation and emergency management

Vona, Marco; Cascini, G.; Mastroberti, Monica; Murgante, Beniamino; Nolè, Gabriele

doi:10.1016/j.ijdrr.2017.05.008

Cited by 14 publications

(10 citation statements)

References 23 publications

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“…However, analytical methods can increasingly rely on innovative sensing technologies (e.g., aerophotogrammetry, Google Maps, drones, remote sensing etc. ), allowing the collection of numerous data remotely with considerable time savings (Vona et al 2017;Fabris et al 2013), and on innovative methods of statistical inference, allowing to derive all the parameters necessary to define the structural models on the basis of a few main measured characteristics (Campostrini et al 2017;Taffarel et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Mechanics-based fragility curves for Italian residential URM buildings

Donà

Carpanese

Follador

et al. 2020

Bull Earthquake Eng

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Seismic risk assessment at the territorial level is now widely recognised as essential for countries with intense seismic activity, such as Italy. Academia is called to give its contribution in order to synergically deepen the knowledge about the various components of this risk, starting from the complex evaluation of vulnerability of the built heritage. In line with this, a mechanics-based seismic fragility model for Italian residential masonry buildings was developed and presented in this paper. This model is based on the classification of the building stock in macro-typologies, defined by age of construction and number of storeys, which being information available at national level, allow simulating damage scenarios and carrying out risk analyses on a territorial scale. The model is developed on the fragility of over 500 buildings, sampled according to national representativeness criteria and analysed through the Vulnus_4.0 software. The calculated fragility functions were extended on the basis of a reference model available in the literature, which provides generic fragilities for the EMS98 vulnerability classes, thus obtaining a fragility model defined on the five EMS98 damage states. Lastly, to assess the reliability of the proposed model, this was used to simulate damage scenarios due to the 2009 L’Aquila earthquake. Overall, the comparison between model results and observed damage showed a good fit, proving the model effectiveness.

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Section: Introductionmentioning

confidence: 99%

Mechanics-based fragility curves for Italian residential URM buildings

Donà

Carpanese

Follador

et al. 2020

Bull Earthquake Eng

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“…For these reasons, the GIS also represents a powerful tool in the field of seismic vulnerability and risk assessment on a large scale, for which all approaches and methodologies must be based on a wide and accurate set of information, being the knowledge of morphological, geometrical, and structural information about a town, its streets, its buildings, and their aggregation, which constitutes crucial input for subsequent analyses [49]. Nevertheless, as already highlighted, at this scale of application, the accessibility, availability, and reliability of data represent the main hurdle to overcome, and the focus should be the data collection procedures and the improvement of methods to investigate the seismic vulnerability of buildings [50]; indeed, in recent years, several procedures have been developed.…”

Section: State Of the Art About Gis-based Building Inventory For The ...mentioning

confidence: 99%

“…The potential of such an IT tool is the possibility to create representative georeferenced databases by manipulating and integrating large sets of different typologies of information derived from the overlap of several data sources and then to implement automatic numerical algorithms for multiple purposes [48]. Indeed, in the GIS environment, it is possible to manage, combine, and analyze geospatial databases about existing building stock and display general information and results about vulnerability assessment, damage, and loss estimation [49][50][51][52]. In addition, the possibility for enhancing the collection data procedure and subsequent seismic vulnerability and risk assessment on a large scale is based on the use of fuzzy logic [53][54][55], machine learning [46,56,57], and Artificial Neural Networks (ANNs) [58][59][60][61].…”

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confidence: 99%

GIS Multisource Data for the Seismic Vulnerability Assessment of Buildings at the Urban Scale

2022

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The paper presents a methodology of extraction, integration and elaboration of data from different sources using the geographic information system (GIS), to realize a georeferenced building database (GBD) useful for the seismic vulnerability assessment of existing buildings on a large scale. Three levels of GIS entities have been defined and equipped with the related information: census section (CS), urban block (UB), and individual building (IB), depending on the level of detail of dataset. Additional information about the typological and structural features has been extracted by CARTIS catalogue, to refine the data associated with each building. The data have been validated using detailed information gathered on a proper sample of buildings, which have been filed and analyzed one-by-one. The alphanumeric format of data allows for the automatic implementation of different methods available in the literature, which provide a qualitative seismic vulnerability index at different scales (whole urban district, an urban block, and a single building). Finally, the 3D representation of data and results have been elaborated, providing a tool easily searchable and constantly implementable. An application has been developed for a case study in Puglia, Italy: Bisceglie, for which 3726 IB have been assessed. The work addresses the issue of the lack of information typical of large-scale applications, exploiting all the available data sources to achieve nearly complete knowledge of existing building stock finalized to a rapid but extensive evaluation of the seismic vulnerability on an urban scale with very low computational efforts and the use of limited resources in terms of time and cost.

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“…In addition, difficulties in establishing residual capacity of earthquake-damaged buildings have contributed to the decision of demolishing buildings (Elwood et al, 2016), which is ecologically unsustainable. Recently, use of unmanned aerial vehicles to perform rapid post-earthquake surveys has been proposed (Vona, Cascini, Mastroberti, Murgante, & Nolè, 2017) to enhance visual inspection. However, determining residual capacities of deteriorated buildings remains challenging (Marquis, Kim, Elwood, & Chang, 2017).…”

Section: Introductionmentioning

confidence: 99%

Measurement-based support for post-earthquake assessment of buildings

Reuland

Lestuzzi

Smith

2019

Structure and Infrastructure Engineering

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After a damaging earthquake, assessment of the residual seismic capacity is required for large parts of the building stock. Increased vulnerability of structures together with the threat of immediate aftershocks call for rapid and objective decision making. Structural identification has the potential to reduce parameter-value uncertainties of physics-based models through interpreting measurement data. Significant amounts of uncertainty are associated with the non-linear behaviour of structures during extreme events such as earthquakes. Therefore, a structural identification methodology that accommodates multiple sources of systematic modelling uncertainties is used. Error-domain model falsification (EDMF) enables structural identification through combining damage grades observed by visual inspection with fundamental frequencies that are derived from ambient vibrations. Parametric uncertainties of a hysteretic model are reduced with the two information sources in order to extrapolate the vulnerability of the building regarding future earthquakes. The applicability of the methodology is shown using measurements made on a mixed reinforced-concrete unreinforced-masonry building tested on a shaking table. Based on nonlinear timehistory analyses involving single-degree-of-freedom models, EDMF leads to more precise, yet robust, vulnerability predictions of earthquake-damaged buildings when compared with prediction ranges that are obtained without data interpretation.

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Characterization of URM buildings and evaluation of damages in a historical center for the seismic risk mitigation and emergency management

Cited by 14 publications

References 23 publications

Mechanics-based fragility curves for Italian residential URM buildings

Mechanics-based fragility curves for Italian residential URM buildings

GIS Multisource Data for the Seismic Vulnerability Assessment of Buildings at the Urban Scale

Measurement-based support for post-earthquake assessment of buildings

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