Post‐earthquake estimates of different ground motion intensity measures for the 2012 Emilia earthquake

Buratti, Nicola; Simoni, Elena; Mazzotti, Claudio

doi:10.1002/eqe.3756

Cited by 3 publications

(3 citation statements)

References 41 publications

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“…Specifically, the severest parameter refers to the seismic characteristic parameter that shows the strongest correlation with structural response under the seismic motions with the same PGV. The parameters preselected in this paper as the severest parameters can be categorized into four classes: acceleration-related parameters, velocity-related parameters, displacement-related parameters, and hybrid parameters [36,37].…”

Section: The Severest Parametersmentioning

confidence: 99%

Methods of Assessing the Damage Capacity of Input Seismic Motions for Underground Structures

Li,

Wei

2024

Buildings

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This paper investigates a method for improving the selection of seismic motions for designing earthquake-resistant underground structures. It is found that PGV alone is unreliable as a predictor of structural damage with increasing earthquake intensity. Therefore, based on characterizing seismic intensity by using PGV, another parameter, referred to here as “the severest parameter”, is introduced to distinguish potential damage capacity for different seismic motions. A numerical model of a soil–underground structure system was established using the finite element software OpenSees. A total of 120 real ground motions were selected for the model, considering the influences of eight different site groups on the underground station and the rupture distances of the input seismic motions. The results show that as seismic intensity increases, substantial variability in the response of underground structures emerges under the same amplitude of PGV, diminishing the effectiveness of the relationship between PGV and structural damage. When assessing the potential damage capacity of seismic motions with similar or close amplitudes of PGV, VSI is an appropriate severest parameter for Class III sites and ASI is suitable for Class II sites. When the correlation coefficient between the severest parameter and the structural response is greater than 0.8, it can be used to reliably assess seismic damage capacity based on the size of the severest parameter.

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Section: The Severest Parametersmentioning

confidence: 99%

Methods of Assessing the Damage Capacity of Input Seismic Motions for Underground Structures

Li,

Wei

2024

Buildings

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“…Over the last decade there has been a dedication to determining the seismic performance of vector-valued IMs [15][16][17][18][19]. The results of many researchers [20][21][22][23][24][25][26] highlight the use of more sophisticated IMs such as those based on spectral shape, since they have the ability to predict the structural response more properly and, above all, a lower uncertainty in buildings which are in the inelastic range [4,6]; therefore, in recent years, Np spectral shape-based IMs have been proposed…”

Section: Introductionmentioning

confidence: 99%

Vector-Valued Intensity Measures Based on Spectral Shape to Predict Seismic Fragility Surfaces in Reinforced Concrete Buildings

et al. 2023

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Although some studies have been conducted to compute fragility surfaces of buildings using vector-valued seismic intensity measures (IMs), in all the cases, the first component of the vector usually is the spectral acceleration at first mode of vibration of the structure Sa(T1). In this study, fragility surfaces of three reinforced concrete buildings (RCB) subjected to narrow-band ground motions obtained from soft soil of Mexico City are computed considering vector-valued IMs based not only on Sa(T1), but also the velocity V(T1), pseudo-velocity Sv(T1), and normalized input energy by the mass EI/m(T1) as the first component. As second component of the vector-valued IMs, the Peak Ground Acceleration (PGA), Peak Ground Velocity (PGV), effective duration (tD), earthquake damage potential (ID) and four Np spectral shape-based parameters obtained through spectral acceleration (NpSa), velocity (NpV), pseudo-velocity (NpSv), and input energy (NpEI) have been analyzed. In order to obtain fragility surfaces, Multinomial Logistic Regression (MLR) was applied. It is observed that those vector-valued IMs based on the spectral shape proxies were more efficient to predict the probability of failure of RCB. For this reason, it is important to consider spectral shape vector-valued IMs in order to reduce uncertainty in the structural response of buildings under earthquakes. Thus, the use of two parameters instead of a single intensity measure improves the efficiency. Moreover, the fragility surfaces can be used for the seismic risk evaluation of buildings.

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“…On the other hand, the structural acceleration, velocity, or displacement response of a building is influenced by its structural properties and earthquake input characteristics [ 18 ]. For example, intensity measures (IM) based on the earthquake ground acceleration have the greatest impact on short-period structures [ 19 ]. Therefore, it is possible to establish an archetype (parametric model) of the buildings built by their main structural characteristics and select the best and minimum set of buildings to be the reference to accurately predict the damage condition of the rest of the buildings under various intensity measures.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Rapid Post-Earthquake Damage Detection of RC Resisting-Moment Frame Buildings

Alcantara

Saito

2023

Sensors

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This study proposes a methodology to predict the damage condition of Reinforced Concrete (RC) resisting-moment frame buildings using Machine Learning (ML) methods. Structural members of six hundred RC buildings with varying stories and spans in X and Y directions were designed using the virtual work method. Sixty thousand time-history analyses using ten spectrum-matched earthquake records and ten scaling factors were carried out to cover the structures’ elastic and inelastic behavior. The buildings and earthquake records were split randomly into training data and testing data to predict the damage condition of new ones. In order to reduce bias, the random selection of buildings and earthquake records was carried out several times, and the mean and standard deviation of the accuracy were obtained. Moreover, 27 Intensity Measures (IM) based on acceleration, velocity, or displacement from the ground and roof sensor responses were used to capture the building’s behavior features. The ML methods used IMs, the number of stories, and the number of spans in X and Y directions as input data and the maximum inter-story drift ratio as output data. Finally, seven Machine Learning (ML) methods were trained to predict the damage condition of buildings, finding the best set of training buildings, IMs, and ML methods for the highest prediction accuracy.

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Post‐earthquake estimates of different ground motion intensity measures for the 2012 Emilia earthquake

Cited by 3 publications

References 41 publications

Methods of Assessing the Damage Capacity of Input Seismic Motions for Underground Structures

Methods of Assessing the Damage Capacity of Input Seismic Motions for Underground Structures

Vector-Valued Intensity Measures Based on Spectral Shape to Predict Seismic Fragility Surfaces in Reinforced Concrete Buildings

Machine Learning-Based Rapid Post-Earthquake Damage Detection of RC Resisting-Moment Frame Buildings

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