Seismic Vulnerability Assessment: Reinforced Concrete Structures

Luca, Flavia De; Verderame, Gerardo Mario

doi:10.1007/978-3-642-36197-5_252-1

Cited by 5 publications

(3 citation statements)

References 51 publications

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“…In the previous study (Luca and Verderame, 2021), a framework was proposed to assess the seismic vulnerability of reinforced concrete structures. All typical modes of failure of reinforced concrete elements were considered in the vulnerability analysis (Luca and Verderame, 2021). The seismic vulnerability of reinforced concrete buildings was studied to consider a group of 91 buildings affected by the macro seismic excitations (Ferreira et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Seismic Vulnerability Assessment of Reinforced Concrete Haunch Beam By Nonlinear Static and Incremental Dynamic Analyses

Haque,

Hoque

2024

Preprint

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This research presents a novel technique of the seismic vulnerability assessment of the reinforced concrete haunch beams in terms of the probability of exceedance of the plastic hinge rotations. For this reason, the nonlinear static and incremental dynamic analyses of various structures with bays have been conducted by the ETABS. The plastic hinge rotation is controlled by the collapse prevention and life safety performance levels of beams and joints, respectively. The roof displacement controls the probability of exceedance of the plastic hinge rotations of beams and joints based on the nonlinear static analysis, which is a new insight of this research. The numerical analysis of ETABS is validated with the previously published work to obtain a good agreement of validation results. The 15-storied structure with 3 bays shows a higher vulnerability and the 4-storied structure shows a lower vulnerability according to the numerical analysis. The ranges of the normalized roof displacement and the peak ground acceleration for higher vulnerability are found to be (0.38 ~ 0.56) and (0.05 ~ 0.09) g, respectively, based on the nonlinear static and incremental dynamic analyses.

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Section: Introductionmentioning

confidence: 99%

Seismic Vulnerability Assessment of Reinforced Concrete Haunch Beam By Nonlinear Static and Incremental Dynamic Analyses

Haque,

Hoque

2024

Preprint

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“…The third stage is brought into action when the structures require a more detailed analysis. The concept of structural activity in seismic activities, defined as non-linear dynamic structural research, is performed here [10].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings

et al. 2020

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Although averting a seismic disturbance and its physical, social, and economic disruption is practically impossible, using the advancements in computational science and numerical modeling shall equip humanity to predict its severity, understand the outcomes, and equip for post-disaster management. Many buildings exist amidst the developed metropolitan areas, which are senile and still in service. These buildings were also designed before establishing national seismic codes or without the introduction of construction regulations. In that case, risk reduction is significant for developing alternatives and designing suitable models to enhance the existing structure’s performance. Such models will be able to classify risks and casualties related to possible earthquakes through emergency preparation. Thus, it is crucial to recognize structures that are susceptible to earthquake vibrations and need to be prioritized for retrofitting. However, each building’s behavior under seismic actions cannot be studied through performing structural analysis, as it might be unrealistic because of the rigorous computations, long period, and substantial expenditure. Therefore, it calls for a simple, reliable, and accurate process known as Rapid Visual Screening (RVS), which serves as a primary screening platform, including an optimum number of seismic parameters and predetermined performance damage conditions for structures. In this study, the damage classification technique was studied, and the efficacy of the Machine Learning (ML) method in damage prediction via a Support Vector Machine (SVM) model was explored. The ML model is trained and tested separately on damage data from four different earthquakes, namely Ecuador, Haiti, Nepal, and South Korea. Each dataset consists of varying numbers of input data and eight performance modifiers. Based on the study and the results, the ML model using SVM classifies the given input data into the belonging classes and accomplishes the performance on hazard safety evaluation of buildings.

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“…Furthermore, the structures which require more attention can be considered for the final step. Here, the implementation of process, which can define the structural behavior under seismic excitations, known as nonlinear dynamic structural analysis, shall be conducted [9].…”

Section: Introductionmentioning

confidence: 99%

Application of Support Vector Machine Modeling for the Rapid Seismic Hazard Safety Evaluation of Existing Buildings

et al. 2020

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The economic losses from earthquakes tend to hit the national economy considerably; therefore, models that are capable of estimating the vulnerability and losses of future earthquakes are highly consequential for emergency planners with the purpose of risk mitigation. This demands a mass prioritization filtering of structures to identify vulnerable buildings for retrofitting purposes. The application of advanced structural analysis on each building to study the earthquake response is impractical due to complex calculations, long computational time, and exorbitant cost. This exhibits the need for a fast, reliable, and rapid method, commonly known as Rapid Visual Screening (RVS). The method serves as a preliminary screening platform, using an optimum number of seismic parameters of the structure and predefined output damage states. In this study, the efficacy of the Machine Learning (ML) application in damage prediction through a Support Vector Machine (SVM) model as the damage classification technique has been investigated. The developed model was trained and examined based on damage data from the 1999 Düzce Earthquake in Turkey, where the building’s data consists of 22 performance modifiers that have been implemented with supervised machine learning.

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Seismic Vulnerability Assessment: Reinforced Concrete Structures

Cited by 5 publications

References 51 publications

Seismic Vulnerability Assessment of Reinforced Concrete Haunch Beam By Nonlinear Static and Incremental Dynamic Analyses

Seismic Vulnerability Assessment of Reinforced Concrete Haunch Beam By Nonlinear Static and Incremental Dynamic Analyses

A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings

Application of Support Vector Machine Modeling for the Rapid Seismic Hazard Safety Evaluation of Existing Buildings

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