On the Seismic Fragility Assessment of Concrete Gravity Dams in Eastern Canada

Segura, Rocio L.; Bernier, Carl; Monteiro, Ricardo; Paultre, Patrick

doi:10.1193/012418eqs024m

Cited by 18 publications

(9 citation statements)

References 40 publications

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“…The records were selected from the PEER NGA-West2 database (Ancheta et al 2013) due to the limited availability of strong ground motion records in the PEER NGA-East database (Goulet et al 2014). Further details on the PSHA and the record selection procedure can be found in Segura et al (2018).…”

Section: Seismic Hazard and Ground Motion Selection Methodsmentioning

confidence: 99%

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: Seismic Hazard and Ground Motion Selection Methodsmentioning

confidence: 99%

Metamodel-Based Seismic Fragility Analysis of Concrete Gravity Dams

2020

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“…In the context of this study, Latin Hypercube sampling (LHS) is adopted to generate the training dataset representing the different configurations of the system under study. This sampling technique was selected because of its ability to divide the desired range of values for each of the m parameters into n equiprobable intervals and then select a sample once from each interval to ensure that the set of samples reflects the entire range of the parameters [29].…”

Section: Experimental Design Matrixmentioning

confidence: 99%

Expected seismic performance of gravity dams using machine learning techniques

Segura

Padgett

Paultre

2021

BNZSEE

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Methods for the seismic analysis of dams have improved extensively in the last several decades. Advanced numerical models have become more feasible and constitute the basis of improved procedures for design and assessment. A probabilistic framework is required to manage the various sources of uncertainty that may impact system performance and fragility analysis is a promising approach for depicting conditional probabilities of limit state exceedance under such uncertainties. However, the effect of model parameter variation on the seismic fragility analysis of structures with complex numerical models, such as dams, is frequently overlooked due to the costly and time-consuming revaluation of the numerical model. To improve the seismic assessment of such structures by jointly reducing the computational burden, this study proposes the implementation of a polynomial response surface metamodel to emulate the response of the system. The latter will be computationally and visually validated and used to predict the continuous relative maximum base sliding of the dam in order to build fragility functions and show the effect of modelling parameter variation. The resulting fragility functions are used to assess the seismic performance of the dam and formulate recommendations with respect to the model parameters. To establish admissible ranges of the model parameters in line with the current guidelines for seismic safety, load cases corresponding to return periods for the dam classification are used to attain target performance limit states.

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“…The response of each sample was analysed for several levels of seismic intensity through an incremental dynamic analysis (IDA). Most of the studies found in the literature concerning seismic fragility assessment for gravity-dam-type structures use the peak ground acceleration (PGA) or the spectral acceleration at fundamental period of the structure as the seismic intensity measure [10,14,15]. Nevertheless, given that the fundamental period depends on the concrete and foundation modulus of elasticity, defined herein as random variables, the spectral acceleration at the fundamental period varies according to the sample considered for the analysis.…”

Section: Levels Of Seismic Intensitymentioning

confidence: 99%

“…The most recent studies were from Bernier et al [10,11], Hariri-Ardebili and Saouma [12], Lallemant et al [13] and Segura et al [14]. In the first two studies [10,11], the finite element method is used to model a single block of a case study dam considering DRF interaction.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, the study performed by Lallemant et al [13] provides a synthesis of the most commonly used methods for fitting fragility curves and highlights some of their significant limitations. Finally, in the study by Segura et al [14], a similar model of the case study dam in Bernier et al [10,11] is used to develop up-to-date fragility curves for the sliding limit states using a record selection method based on the generalised conditional intensity measure approach. These fragility functions are then combined with the recently developed regional hazard data to evaluate the annual risk, which is measured in terms of the unconditional probability of limit state exceedance.…”

Section: Introductionmentioning

confidence: 99%

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Modelling and Characterizing a Concrete Gravity Dam for Fragility Analysis

et al. 2019

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Most gravity dams have been designed and built during the past century with methods of analysis that are now considered inadequate. In recent decades, knowledge of seismology, structural dynamics and earthquake engineering has greatly evolved, leading to the evaluation of existing dams to ensure public safety. This study proposes a methodology for the proper modelling and characterisation of the uncertainties to assess the seismic vulnerability of a dam-type structure. This study also includes all the required analyses and verifications of the numerical model prior to performing a seismic fragility analysis and generating the corresponding fragility curves. The procedure presented herein also makes it possible to account for the uncertainties associated with the modelling parameters as well as the randomness in the seismic solicitation. The methodology was applied to a case study dam in Eastern Canada, whose vulnerability was assessed against seismic events with characteristics established by the current safety guidelines.

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On the Seismic Fragility Assessment of Concrete Gravity Dams in Eastern Canada

Cited by 18 publications

References 40 publications

Metamodel-Based Seismic Fragility Analysis of Concrete Gravity Dams

Metamodel-Based Seismic Fragility Analysis of Concrete Gravity Dams

Expected seismic performance of gravity dams using machine learning techniques

Modelling and Characterizing a Concrete Gravity Dam for Fragility Analysis

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