Typological fragility curves for RC buildings: influence of damage index and building sample selection

Zucconi, Maria; Romano, Fabio; Ferracuti, Barbara

doi:10.1016/j.engstruct.2022.114627

Cited by 13 publications

(5 citation statements)

References 70 publications

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“…This system has been used in dozens of studies to develop exposure models (e.g. Crowley et al, 2020; Yepes et al, 2023), classify specific building classes (Koren and Rus, 2023; Opabola et al, 2022), and define structural classes for the scoring and development of fragility/vulnerability functions (Gentile et al, 2022; Martins and Silva, 2020; Zucconi et al, 2022). Furthermore, the employment of a well-established building taxonomy enables merging imagery from other databases following the same classification system.…”

Section: Classification Of the Building Stockmentioning

confidence: 99%

A building imagery database for the calibration of machine learning algorithms

Silva,

Sousa,

Ribeiro Gouveia

et al. 2024

Earthquake Spectra

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In the last decades, most efforts to catalog and characterize the built environment for multi-hazard risk assessment have focused on the exploration of census data, cadastral data sets, and local surveys. Typically, these sources of information are not updated regularly and lack sufficient information to characterize the seismic vulnerability of the building stock. Some recent efforts have demonstrated how machine learning algorithms can be used to automatically recognize specific architectural and structural features of buildings. However, such methods require large sets of labeled images to train, verify, and test the algorithms. This article presents a database of 5276 building images from a parish in Lisbon (Alvalade), whose buildings have been classified according to a uniform taxonomy. This database can be used for the testing and calibration of machine learning algorithms, as well as for the direct assessment of earthquake risk in Alvalade. The data are accessible through an open Github repository (DOI: 10.5281/zenodo.7625940).

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Section: Classification Of the Building Stockmentioning

confidence: 99%

A building imagery database for the calibration of machine learning algorithms

Silva,

Sousa,

Ribeiro Gouveia

et al. 2024

Earthquake Spectra

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“…Los estados de daño se definen por separado para los sistemas estructurales y no estructurales de un edificio, (Kircher et al, 2006). El daño se describe mediante uno de cuatro niveles de daño discretos: leve, moderado, severo o completo (Zucconi et al, 2022). Por supuesto, el daño real a los edificios varía como una función continua de la demanda sísmica (Stumpf et al, 2019).…”

Section: Estados De Dañounclassified

Evaluación probabilística de fragilidad sísmica para las estructuras de HA, en los barrios periféricos occidentales de la ciudad de Loja.

2023

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Los sismos representan un peligro natural importante, que afecta regularmente las vidas y el entorno construido en áreas propensas a terremotos en todo el mundo, provocando pérdidas sociales y económicas, (Guha-Sapir et al., 2016). La metodología HAZUS exhibe un enfoque simple, preciso y avanzado para el desarrollo de curvas de fragilidad, y la estimación de daño (leve, moderado, extenso y colapso) de las infraestructuras ante la acción sísmica, (Patel & Vasanwala, 2019). En el Ecuador los gobiernos municipales no actúan como un agente regulador urbano eficaz, es por cuanto, aún existe la predominancia de la construcción informal. A nivel local, en la zona occidental de la ciudad de Loja, se localizan barrios periféricos con edificaciones de hormigón armado, los cuales en un gran porcentaje se encuentran construidos sin un diseño sismorresistente, por esta razón, este trabajo de investigación pretende analizar y estimar la probabilidad de daño sísmico.

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“…Caglar Goksu et al [17] investigated research by compiling the data of RC columns incorporating recycled concrete aggregate to generate fragility functions. Maria Zucconi et al [18] conducted a detailed study to develop fragility curves specifically for RC-frame buildings.…”

Section: Introductionmentioning

confidence: 99%

Seismic Damage Probability Assessment of Existing Reinforced Concrete School Buildings in Afghanistan

Sharafi,

Saito

2024

Buildings

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Existing Reinforced Concrete School buildings with low earthquake resistance may suffer structural failure or severe damage in a catastrophic seismic event. Ascertaining earthquake resistance in existing school buildings is vital to confirming the safety of students, teachers, and all school members. Reinforced concrete (RC) has been used significantly for numerous years as the primary material due to its easy access and low cost-effectiveness in construction. The current research focused on analyzing the existing RC school buildings designed and constructed in various regions of Afghanistan over the last three decades. Seismic fragility curves, which are generated from incremental dynamic analysis (IDA), have been used to evaluate the damage probability of RC school buildings against earthquake ground motions. In this investigation, 34 RC school buildings were selected from an extensive database and subsequently classified as either A-type or B-type based on specific criteria, including design details and construction year. Following this classification, an assessment of the seismic damage probability for these buildings was conducted using probabilistic models based on IDA curves. The results indicate that A-type school buildings with newer construction are less prone to damage compared to B-type school buildings, showing improved resilience. Especially the B-type buildings in seismic Zone-I are found to be highly vulnerable under the maximum considered earthquake scenarios.

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Typological fragility curves for RC buildings: influence of damage index and building sample selection

Cited by 13 publications

References 70 publications

A building imagery database for the calibration of machine learning algorithms

A building imagery database for the calibration of machine learning algorithms

Evaluación probabilística de fragilidad sísmica para las estructuras de HA, en los barrios periféricos occidentales de la ciudad de Loja.

Seismic Damage Probability Assessment of Existing Reinforced Concrete School Buildings in Afghanistan

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