Seismic response of R/C structures subjected to simulated ground motions compatible with design spectrum

Jun, Dae-Han

doi:10.1002/tal.658

Cited by 9 publications

(6 citation statements)

References 11 publications

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“…As for the ε 1 setting in Equation , this is set to 30% by the Korea Electric Power Industry Code (KEPIC) . In this study, ε 1 was set to 15% to increase the effectiveness of the artificial seismic wave, while ε 2 was set to 5% based on a previous study on artificial seismic waves …”

Section: Applicationmentioning

confidence: 99%

“…37 In this study, ε 1 was set to 15% to increase the effectiveness of the artificial seismic wave, while ε 2 was set to 5% based on a previous study on artificial seismic waves. 38 Based on the proposed method, artificial seismic waves were created using EQmaker, an EQ generation software program. 39 In this instance, nine artificial seismic waves with different duration ranges were created.…”

Section: Description Of Asce Benchmark Modelmentioning

confidence: 99%

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Seismic response prediction method for building structures using convolutional neural network

Park

2020

Struct Control Health Monit

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Summary In this study, a method of predicting the seismic responses of building structures based on a convolutional neural network (CNN) is proposed. In the method, the time histories of acceleration responses previously measured in a building during earthquakes are used in the CNN input layer, with the corresponding time histories of the displacement responses being used in the CNN output layer. The correlations between the features automatically extracted from the acceleration responses by the convolution and pooling operations in the various CNN layers and the displacement responses in the CNN output layer are established through CNN training. The trained CNN predicts the displacement responses for a future earthquake event using only the measured acceleration responses. To verify the validity of the proposed method, a numerical study on the ASCE benchmark model and an experimental study on a reinforced concrete frame structure are conducted. In the numerical study, the structural responses of the ASCE model subject to artificial earthquakes are utilized for CNN training, and the performance of the trained CNN is verified through seismic response prediction. In the experimental study, shaking table tests are performed for various earthquakes to measure the seismic responses of the reinforced concrete test structure. The seismic response prediction performance of the proposed method is examined using CNN trained with the measured seismic responses. Furthermore, the performances of CNNs trained with different numbers of datasets generated by the proposed data generation method based on data overlapping with the same data pool are discussed with related to the number of CNN training iteration number.

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Section: Applicationmentioning

confidence: 99%

Section: Description Of Asce Benchmark Modelmentioning

confidence: 99%

Seismic response prediction method for building structures using convolutional neural network

Park

2020

Struct Control Health Monit

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“…Equation determines the effectiveness of an artificial seismic wave by calculating the root mean square deviation between the peak acceleration of an artificial seismic wave for

T_{i}

and the acceleration of the design response spectrum. Jun () set the value of ε in the equation as 0.05, with a damping ratio of 5%. Consequently, if an artificially created seismic wave satisfies Equations and , it is utilized as effective aftershock data.…”

Section: Generation Of Earthquake Sequencesmentioning

confidence: 99%

Fragility Assessment Model of Building Structures Using Characteristics of Artificial Aftershock Motions

Park

Choi

2018

Computer aided Civil Eng

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Any additional loads applied to a damaged structure can aggravate its instability and thus, the impact of successive earthquakes need to be considered. This study proposed a quantitative assessment model for the fragility of a damaged structure subjected to aftershock. Mean period and the strong motion duration were considered as characteristics of earthquake motions. Simulation models of two reinforced concrete structures and one steel structure were selected to examine the applicability of the model. Based on the suggested fragility and residual deformation coefficients, critical earthquake sequences for each structure were identified. The proposed model was efficient in selecting critical earthquake sequences by using the limited number of aftershocks, and these sequences are expected to be useful indicators in the establishment of a retrofit plan according to the predicted structural response and target performance levels.

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“…Implication of the model in engineering application to synthesizing design accelerograms was then discussed. Also, there is other method can be used to generate a DRSCEM by selecting the phases of the ground motions from particular observed earthquakes [6,26,27].…”

Section: Introductionmentioning

confidence: 99%

Definition of Yield Seismic Coefficient Spectrum Considering the Uncertainty of the Earthquake Motion Phase

et al. 2019

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Earthquake engineers are typically faced with the challenge of safely and economically designing structures in highly uncertain seismic environments. Yield strength demand spectra provide basic information for the seismic design of structures and take nonlinear behavior into account. The designed structures, however, must be checked for seismic performance through dynamic analysis. Design-response spectra compatible earthquake motions (DRSCEM) are commonly used for this purpose. Because DRSCEM are strongly affected by the assigned phase characteristics, in this paper, we simulate realistic earthquake motion phase based on a stochastic process that modifies fractional Brownian motion (fBm). The parameters that control this process were determined via regression equations as functions of the earthquake magnitude and epicenter distance, which were obtained through a regression analysis that was performed on data from a database of recorded ground motions. After validating the efficiency of the modeled phase spectrum, large numbers of DRSCEMs were simulated with which several ductility demand spectra were obtained. By statistically analyzing these results, a rigorous yield seismic coefficient demand spectrum is proposed.

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Seismic response of R/C structures subjected to simulated ground motions compatible with design spectrum

Cited by 9 publications

References 11 publications

Seismic response prediction method for building structures using convolutional neural network

Seismic response prediction method for building structures using convolutional neural network

Fragility Assessment Model of Building Structures Using Characteristics of Artificial Aftershock Motions

Definition of Yield Seismic Coefficient Spectrum Considering the Uncertainty of the Earthquake Motion Phase

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