Expected seismic performance of gravity dams using machine learning techniques

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dc.citation.firstpage58
dc.citation.issueNumber2
dc.citation.journalTitleBulletin of the New Zealand Society for Earthquake Engineering
dc.citation.lastpage68
dc.citation.volumeNumber54
dc.contributor.authorSegura, Rocio
dc.contributor.authorPadgett, Jamie
dc.contributor.authorPaultre, Patrick
dc.date.accessioned2021-06-25T13:34:53Z
dc.date.available2021-06-25T13:34:53Z
dc.date.issued2021
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.
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.
dc.identifier.digital1533-Article-3003-2-10-20210528
dc.identifier.doihttps://doi.org/10.5459/bnzsee.54.2.58-68
dc.identifier.urihttps://hdl.handle.net/1911/110833
dc.language.isoeng
dc.publisherNew Zealand Society for Earthquake Engineering
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleExpected seismic performance of gravity dams using machine learning techniques
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
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