An archetype-based automated procedure to derive global-local seismic fragility of masonry building aggregates: META-FORMA-XL

Ruggieri, Sergio; Liguori, Francesco S.; Leggieri, Valeria; Bilotta, Antonio; Madeo, Antonio; Casolo, Siro; Uva, Giuseppina

doi:10.1016/j.ijdrr.2023.103903

Cited by 37 publications

(13 citation statements)

References 38 publications

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“…This devastating effect could be avoided in the future by using an appropriate structural typology, calculation models that adequately model the earthquake and the consideration of an accelerogram from a safety perspective. The analysis of seismic vulnerability at the urban scale, which allows risk analysis and quantification of expected losses, is also a suitable strategy [15].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis and Evaluation of Seismic Response in Structures: Perspectives from Non-Linear Dynamic Analysis to Pushover Analysis

Rodríguez,

Rodríguez Pérez,

López

et al. 2024

Applied Sciences

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This study presents a detailed comparative analysis of different methods for evaluating seismic response in structures, focusing on maximum displacements and collapse assessment. The results obtained through modal spectral analysis, non-linear dynamic analysis, and the incremental pushover analysis applied to a specific structure are compared. It has been found that the choice of time step and the consideration of ductility are critical for obtaining accurate predictions. The results of the non-linear dynamic analysis of the building’s response indicate that an earthquake equivalent to the one that affected the city of Lorca (southeast Iberian Peninsula) in 2011 would have a devastating impact on the studied structure, highlighting the importance of the finite element method modelling in predicting the formation of plastic hinges and assessing structural safety. These findings highlight the importance of utilising multiple analysis approaches and detailed modelling to fully understand the seismic behaviour of structures and ensure adequate resistance and stability to extreme events.

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Section: Introductionmentioning

confidence: 99%

Comparative Analysis and Evaluation of Seismic Response in Structures: Perspectives from Non-Linear Dynamic Analysis to Pushover Analysis

Rodríguez,

Rodríguez Pérez,

López

et al. 2024

Applied Sciences

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“…At present, there are five main ways to obtain regional building construction information: based on census data [6], based on geographic information systems (GIS) [25], based on site surveys [26], based on design information [24], and based on empirical data [21]. Building information obtained by census data is usually rough and rarely involves detailed building construction information, such as HAZUS, which divides buildings into 36 categories according to their structural characteristics and use functions and determines the ratio of the number of each type of building in the region based on zoning census data, and determines the difference in losses between different individuals of the same kind of building by floor area, which cannot consider the impact of the differences in the internal construction on the seismic performance and earthquake losses.…”

Section: Introductionmentioning

confidence: 99%

“…The GIS-based approach faces the same problem. Ruggieri et al [26] conducted field surveys to gather architectural information on masonry structures, and the collected information was classified into two categories: deterministic parameters and uncertain parameters. Archetypes, which are ideal representations of existing building stock, were created to model the geometrical and mechanical uncertainties of buildings.…”

Section: Introductionmentioning

confidence: 99%

A Story-Based Random Structure Modeling Method for Regional Buildings Earthquake Loss Estimation

Yang,

Lu,

Xiong

2024

Applied Sciences

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To estimate earthquake losses for regional buildings, it is important to consider the varying seismic performances of different types of buildings. To achieve this, a dynamic elastic–plastic analysis based on detailed structural modeling is more accurate than the capacity spectrum method based on a single degree of freedom to obtain the response and damage of various structures under seismic activity and provide a more precise estimate of earthquake losses for buildings. Detailed information about the building facilities is necessary to create a fine structural model. However, obtaining precise actual structural details can be challenging with existing methods, especially when there are a large number of buildings to consider. This paper proposes a new method called story-based random structure (SBRS) modeling to address this issue, which is based on the common and reasonable layout of the structure. The process involves choosing design parameters that can represent the structural arrangement of a building as variables. These parameters include the materials’ type and strength, proportions, the components’ size, and other relevant factors. The values for these variables are determined based on engineering experience and design specifications. The range of values for these variables is also determined to ensure that all design requirements are met. Finally, the Latin Hypercubic Sampling (LHS) method is used to randomly sample and combine the variables, establishing a detailed structural model of the building with structural uncertainty. According to the results of the analysis, this method can simulate the structure and component information of a building by using a probabilistic approach, even without knowing the specific structural design. This method can be based on a small amount of readily available building information and solves the problem of the rapid and refined modeling of a large number of buildings at the regional scale. It also makes it possible to estimate the earthquake loss of regional buildings based on their seismic capacity.

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“…Several studies on seismic vulnerability at the urban scale follow a mechanical approach based on the consideration of ideal numerical models of existing building typologies [17] and aggregates of buildings [18]. Recurrent geometrical, structural, and technological features of buildings can be identified using the geographic information system [19].…”

Section: Introductionmentioning

confidence: 99%

Seismic Vulnerability Assessment at an Urban Scale by Means of Machine Learning Techniques

Ferranti,

Greco,

Pluchino

et al. 2024

Buildings

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Seismic vulnerability assessment in urban areas would, in principle, require the detailed modeling of every single building and the implementation of complex numerical calculations. This procedure is clearly difficult to apply at an urban scale where many buildings must be considered; therefore, it is essential to have simplified, but at the same time reliable, approaches to vulnerability assessment. Among the proposed strategies, one of the most interesting concerns is the application of machine learning algorithms, which are able to classify buildings according to their vulnerability on the basis of training procedures applied to existing datasets. In this paper, machine learning algorithms were applied to a dataset which collects and catalogs the structural characteristics of a large number of buildings and reports the damage observed in L’Aquila territory during the intense seismic activity that occurred in 2009. A combination of a trained neural network and a random forest algorithm allows us to identify an opportune “a-posteriori” vulnerability score, deduced from the observed damage, which is compared to an “a-priori” vulnerability one, evaluated taking into account characteristic indexes for building’s typologies. By means of this comparison, an inverse approach to seismic vulnerability assessment, which can be extended to different urban centers, is proposed.

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An archetype-based automated procedure to derive global-local seismic fragility of masonry building aggregates: META-FORMA-XL

Cited by 37 publications

References 38 publications

Comparative Analysis and Evaluation of Seismic Response in Structures: Perspectives from Non-Linear Dynamic Analysis to Pushover Analysis

Comparative Analysis and Evaluation of Seismic Response in Structures: Perspectives from Non-Linear Dynamic Analysis to Pushover Analysis

A Story-Based Random Structure Modeling Method for Regional Buildings Earthquake Loss Estimation

Seismic Vulnerability Assessment at an Urban Scale by Means of Machine Learning Techniques

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