The dependence of conditional spectra on the choice of target periods

Azarbakht, Alireza; Ghodrati, A.R.

doi:10.24200/sci.2017.4255

Cited by 2 publications

(2 citation statements)

References 18 publications

(25 reference statements)

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“…where m lnSa M, R, T ð Þ and s lnSa (T) are the predicted mean and standard deviation of lnSa at a given period, respectively, and lnSa(T) is the log of the spectral acceleration of interest. 35,36 From basic probability theory, the annual frequency of exceedance (n) for lnSa . x can be written as:…”

Section: Seismic Hazard Curvementioning

confidence: 99%

Estimating collapse risk and reliability of concrete moment frame structure using response surface method and hybrid of artificial neural network with particle swarm optimization algorithm

Bayari

Shabakhty

Abadi

2021

Proceedings of the Institution of Mechanical Engineers, Part O:

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Structural collapse performance assessment has been at the center of many researchers’ interest due to complications of this phenomenon and uncertainties involved in modeling the simulation of the structural collapse response. This research aims to predict the structural collapse responses including mean collapse capacity, collapse standard deviation, and collapse drift by considering modeling uncertainties and then estimating collapse fragility curves, collapse risk, and reliability using Response Surface Method (RSM) and Artificial Neural Network (ANN). Modeling uncertainties for evaluating collapse responses are the parameters of the modified Ibarra-Krawinkler moment-rotation curve. Moreover, to analyze the structural uncertainty, the correlation between the model parameters in one component and between two structural components was considered. The Latin Hypercube Sampling (LHS) method and Cholesky decomposition were used to produce independent and dependent random variables, respectively. To predict the collapse responses of the structure, taking into account the uncertainties, as the number of uncertainties increases, the number of simulations for the uncertainties also increases, leading to a significant increase in the computational effort to estimate the structural responses, in the presence of a limited number of samples for uncertainties, a hybrid of ANN with PSO algorithm was used to reduce the computational effort in order to estimate the collapse fragility curves, collapse risk, and structural reliability. The results show that structural collapse responses can be predicted with appropriate accuracy by producing a limited number of samples for uncertainties and using an ANN-PSO algorithm.

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Section: Seismic Hazard Curvementioning

confidence: 99%

Estimating collapse risk and reliability of concrete moment frame structure using response surface method and hybrid of artificial neural network with particle swarm optimization algorithm

Bayari

Shabakhty

Abadi

2021

Proceedings of the Institution of Mechanical Engineers, Part O:

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show abstract

“…To complement this dependency, Baker and Cornell [11] proposed the conditional mean spectrum associated with epsilon (CMS-ε); Baker [12] provided the CMS as a ground motion selection tool, and Mousavi et al [13] proposed the eta-based CMS. Since these methods require selection of a conditioning period, the effects of the period are examined [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Seismic Diversity and Robustness of Nuclear Power Plants

Katayama

Ohtori

Sakai

et al. 2021

Journal of Nuclear Science and Technology

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The occurrence of common cause failures poses a critical threat to the seismic safety of nuclear power plants. The installation of diverse components such as isolation systems is believed to be a promising strategy to avoid such failures, especially in the context of disasters such as the Fukushima accident. Although the effectiveness of implementing diverse components in enhancing the seismic safety has been qualitatively evaluated, a quantitative examination considering the system reliability has not been performed yet. Therefore, in this study, a method to quantitatively evaluate the effectiveness of diverse components in preventing failures was developed. The definitions of the seismic diversity and robustness in different domains were examined. Subsequently, the proposed approach was used to evaluate the effectiveness of the seismic diversity strategy on enhancing the system reliability at 11 sites in Japan. Moreover, the seismic robustness of the system was assessed. The results indicated that the optimal constitution of diverse components depends on the seismic environment at each site, and the seismic diversity is a more effective strategy compared to that of multiplicity to ensure the seismic safety of nuclear power plants.

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The dependence of conditional spectra on the choice of target periods

Cited by 2 publications

References 18 publications

Estimating collapse risk and reliability of concrete moment frame structure using response surface method and hybrid of artificial neural network with particle swarm optimization algorithm

Estimating collapse risk and reliability of concrete moment frame structure using response surface method and hybrid of artificial neural network with particle swarm optimization algorithm

Evaluating the Seismic Diversity and Robustness of Nuclear Power Plants

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