Probabilistic seismic analysis of concrete dry cask structures

Sichani, Majid Ebad; Padgett, Jamie E.; Bisadi, Vahid

doi:10.1016/j.strusafe.2018.03.001

Cited by 30 publications

(20 citation statements)

References 30 publications

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“…This combination is justified by the significant randomness that characterizes not only the earthquake excitation but also the structural system itself (e.g., stochastic variations in the material properties, degradation due to aging, and temperature fluctuation, etc.). Surrogate modeling techniques within a seismic fragility framework have found recent applications for the safety assessment of buildings and bridges, among other structures (Mangalathu and Jeon 2018;Sichani et al 2017;Kameshwar and Padgett 2014;Ghosh et al 2013;Seo and Linzell 2013;Seo et al 2012). Even though many of these studies considered several seismic intensity measures (IMs) and model parameters (MPs) for building the metamodels to predict the response of the structure, most of them do not clearly depict the influence of all the considered parameters in the form of multivariate fragility functions from the respective metamodels.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Up to 2nd-order polynomials shall suffice for responses characterized by low curvatures, while 3rd-and 4th-degree polynomials including two factor interactions are more appropriate for significant curvatures (Murphy 2012). This approach is considered because past studies have shown them to be efficient and accurate for concrete gravity dam seismic performance as well as for metamodels of other complex structures (Hariri-Ardebili 2018; Seo and Park 2017;Sichani et al 2017).…”

Section: Polynomial Response Surface: Prsmentioning

confidence: 99%

“…where CRF, DR, and CRC = model parameters corresponding to the concrete-rock angle of friction, drain efficiency, and concreterock cohesion, respectively; PGV = peak ground velocity; PGA V = peak ground acceleration in the vertical direction; and I a = Arias intensity. A normally distributed model error term v with zero mean and standard deviation equal to the RMSE is added to the selected surrogate model to reflect the lack of fit (Barnston 1992;Kameshwar and Padgett 2014;Sichani et al 2017). The stepwise regression algorithm in MATLAB R2019b was formally implemented together with PRS to improve the task of manually selecting the predictors.…”

Section: Polynomial Response Surface Metamodelmentioning

confidence: 99%

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Metamodel-Based Seismic Fragility Analysis of Concrete Gravity Dams

2020

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Probabilistic methods, such as fragility analysis, have been developed as a promising alternative for the seismic assessment of dam-type structures. However, given the costly reevaluation of the numerical model simulations, the effect of the model parameters likely to affect the seismic fragility of the system is frequently overlooked. Acknowledging the lack of the thorough exploration of different machine learning techniques to develop surrogates or metamodels that efficiently approximate the seismic response of dams, this study provides insight on viable metamodels for the seismic assessment of gravity dams for use in fragility analysis. The proposed methodology to generate multivariate fragility functions offers efficiency while accounting for the most critical model parameter variation influencing the dam seismic fragility. From the analysis of these models, practical design recommendations can be formulated. The procedure presented herein is applied to a case study dam in northeastern Canada, where the polynomial response surface of order 4 (PRS O 4 ) came up as the most viable metamodel among those considered. Its fragility is assessed through comparison with the current safety guidelines to establish a range of usable model parameter values in terms of the concrete-rock angle of friction, drain efficiency, and concrete-rock cohesion. ; separate discussions must be submitted for individual papers. This paper is part of the Journal of Structural Engineering, © ASCE, ISSN 0733-9445. © ASCE 04020121-1 J. Struct. Eng. J. Struct. Eng., 2020, 146(7): 04020121 Downloaded from ascelibrary.org by 44.224.250.200 on 07/05/20. Copyright ASCE. For personal use only; all rights reserved. © ASCE 04020121-2 J. Struct. Eng. J. Struct. Eng., 2020, 146(7): 04020121 Downloaded from ascelibrary.org by 44.224.250.200 on 07/05/20. Copyright ASCE. For personal use only; all rights reserved. © ASCE 04020121-6 J. Struct. Eng. J. Struct. Eng., 2020, 146(7): 04020121 Downloaded from ascelibrary.org by 44.224.250.200 on 07/05/20. Copyright ASCE. For personal use only; all rights reserved. © ASCE 04020121-7 J. Struct. Eng. J. Struct. Eng., 2020, 146(7): 04020121 Downloaded from ascelibrary.org by 44.224.250.200 on 07/05/20. Copyright ASCE. For personal use only; all rights reserved. © ASCE 04020121-10 J. Struct. Eng. J. Struct. Eng., 2020, 146(7): 04020121 Downloaded from ascelibrary.org by 44.224.250.200 on 07/05/20. Copyright ASCE. For personal use only; all rights reserved. © ASCE 04020121-11 J. Struct. Eng. J. Struct. Eng., 2020, 146(7): 04020121 Downloaded from ascelibrary.org by 44.224.250.200 on 07/05/20. Copyright ASCE. For personal use only; all rights reserved. © ASCE 04020121-12 J. Struct. Eng. J. Struct. Eng., 2020, 146(7): 04020121 Downloaded from ascelibrary.org by 44.224.250.200 on 07/05/20. Copyright ASCE. For personal use only; all rights reserved. © ASCE 04020121-13 J. Struct. Eng. J. Struct. Eng., 2020, 146(7): 04020121 Downloaded from ascelibrary.org by 44.224.250.200 on 07/05/20. Copyright ASCE. For personal use onl...

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Section: Literature Reviewmentioning

confidence: 99%

Section: Polynomial Response Surface: Prsmentioning

confidence: 99%

Section: Polynomial Response Surface Metamodelmentioning

confidence: 99%

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Metamodel-Based Seismic Fragility Analysis of Concrete Gravity Dams

2020

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“…Recently, relevant works have assessed the seismic performance of rocking structures in a probabilistic way . Fragility curves that estimate overturning probability and rotation demand were developed by Dimitrakopoulos and Paraskeva for rigid rocking structures by using bivariate IMs .…”

Section: Introductionmentioning

confidence: 99%

“…25,[33][34][35] Recently, relevant works have assessed the seismic performance of rocking structures in a probabilistic way. 30,[36][37][38][39][40] Fragility curves that estimate overturning probability and rotation demand were developed by Dimitrakopoulos and Paraskeva 36 for rigid rocking structures by using bivariate IMs. Their study concludes that the rocking overturning tendency depends primarily on the velocity characteristics of ground motions.…”

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confidence: 99%

Seismic fragilities of single‐column highway bridges with rocking column‐footing

Xie

Zhang

DesRoches

et al. 2019

Earthq Engng Struct Dyn

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Summary Rocking isolation has been increasingly studied as a promising design concept to limit the earthquake damage of civil structures. Despite the difficulties and uncertainties of predicting the rocking response under individual earthquake excitations (due to negative rotational stiffness and complex impact energy loss), in a statistical sense, the seismic performance of rocking structures has been shown to be generally consistent with the experimental outcomes. To this end, this study assesses, in a probabilistic manner, the effectiveness of using rocking isolation as a retrofit strategy for single‐column concrete box‐girder highway bridges in California. Under earthquake excitation, the rocking bridge could experience multi‐class responses (eg, full contacted or uplifting foundation) and multi‐mode damage (eg, overturning, uplift impact, and column nonlinearity). A multi‐step machine learning framework is developed to estimate the damage probability associated with each damage scenario. The framework consists of the dimensionally consistent generalized linear model for regression of seismic demand, the logistic regression for classification of distinct response classes, and the stepwise regression for feature selection of significant ground motion and structural parameters. Fragility curves are derived to predict the response class probabilities of rocking uplift and overturning, and the conditional damage probabilities such as column vibrational damage and rocking uplift impact damage. The fragility estimates of rocking bridges are compared with those for as‐built bridges, indicating that rocking isolation is capable of reducing column damage potential. Additionally, there exists an optimal slenderness angle range that enables the studied bridges to experience much lower overturning tendencies and significantly reduced column damage probabilities at the same time.

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Uniform risk spectra for rocking structures

Manzo

Lachanas

Vassiliou

et al. 2022

Earthq Engng Struct Dyn

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This paper presents uniform risk spectra for zero stiffness bilinear elastic (ZSBE) systems. The ZSBE oscillator is a bilinear elastic system with zero post‐“yield” stiffness that satisfactorily predicts the response of different systems with negative lateral stiffness (e.g., free‐standing or restrained rocking blocks). It can be described by a single parameter; thus, it is simpler to produce its spectrum. Using the ZSBE proxy, this paper provides the uniform risk spectra for sites in six locations in Europe. The spectra are constructed using two distinct intensity measures (IMs): peak ground velocity (PGV) and peak ground acceleration (PGA). The efficiency of both IMs at different ranges of displacement demands is discussed and analytical approximations of the spectra are proposed.

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Probabilistic seismic analysis of concrete dry cask structures

Cited by 30 publications

References 30 publications

Metamodel-Based Seismic Fragility Analysis of Concrete Gravity Dams

Metamodel-Based Seismic Fragility Analysis of Concrete Gravity Dams

Seismic fragilities of single‐column highway bridges with rocking column‐footing

Uniform risk spectra for rocking structures

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