Expected seismic performance of gravity dams using machine learning techniques

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dc.citation.firstpage58en_US
dc.citation.issueNumber2en_US
dc.citation.journalTitleBulletin of the New Zealand Society for Earthquake Engineeringen_US
dc.citation.lastpage68en_US
dc.citation.volumeNumber54en_US
dc.contributor.authorSegura, Rocioen_US
dc.contributor.authorPadgett, Jamieen_US
dc.contributor.authorPaultre, Patricken_US
dc.date.accessioned2021-06-25T13:34:53Zen_US
dc.date.available2021-06-25T13:34:53Zen_US
dc.date.issued2021en_US
dc.description.abstractMethods 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.en_US
dc.identifier.citationSegura, Rocio, Padgett, Jamie and Paultre, Patrick. "Expected seismic performance of gravity dams using machine learning techniques." <i>Bulletin of the New Zealand Society for Earthquake Engineering,</i> 54, no. 2 (2021) New Zealand Society for Earthquake Engineering: 58-68. https://doi.org/10.5459/bnzsee.54.2.58-68.en_US
dc.identifier.digital1533-Article-3003-2-10-20210528en_US
dc.identifier.doihttps://doi.org/10.5459/bnzsee.54.2.58-68en_US
dc.identifier.urihttps://hdl.handle.net/1911/110833en_US
dc.language.isoengen_US
dc.publisherNew Zealand Society for Earthquake Engineeringen_US
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleExpected seismic performance of gravity dams using machine learning techniquesen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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