Simulation of non-stationary non-Gaussian random fields from sparse measurements using Bayesian compressive sampling and Karhunen-Loève expansion

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dc.citation.journalTitleSTRUCTURAL SAFETY
dc.contributor.authorMontoya, S.
dc.contributor.authorTengyuan Zhao
dc.contributor.authorYue Hu
dc.contributor.authorYu Wang
dc.contributor.authorKok-Kwang Phoon
dc.contributor.researchgroupMecánica Aplicadaspa
dc.date.accessioned2019-03-10
dc.date.accessioned2021-04-16T20:10:41Z
dc.date.available2021-04-16T20:10:41Z
dc.date.issued2019-03-20
dc.date.submitted2018-09-14
dc.description.abstractThe first step to simulate random fields in practice is usually to obtain or estimate random field parameters, such as mean, standard deviation, correlation function, among others. However, it is difficult to estimate these parameters, particularly the correlation length and correlation functions, in the presence of sparse measurement data. In such cases, assumptions are often made to define the probabilistic distribution and correlation structure (e.g. Gaussian distribution and stationarity), and the sparse measurement data are only used to estimate the parameters tailored by these assumptions. However, uncertainty associated with the degree of imprecision in this estimation process is not taken into account in random field simulations. This paper aims to address the challenge of properly simulating non-stationary non-Gaussian random fields, when only sparse data are available. A novel method is proposed to simulate non-stationary and non-Gaussian random field samples directly from sparse measurement data, bypassing the difficulty in random field parameter estimation from sparse measurement data. It is based on Bayesian compressive sampling and Karhunen–Loève expansion. First, the formulation of the proposed generator is described. Then, it is illustrated through simulated examples, and tested with wind speed time series data. The results show that the proposed method is able to accurately depict the underlying spatial correlation from sparse measurement data for both non-Gaussian and non-stationary random fields. In addition, the proposed method is able to quantify the uncertainty related to random field parameter estimation from the sparse measurement data and propagate it to the generated random field. © 2019 Elsevier Ltdeng
dc.identifierhttps://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=8597
dc.identifier.doi10.1016/j.strusafe.2019.03.006
dc.identifier.issn01674730
dc.identifier.issn18793355
dc.identifier.otherWOS;000467512600006
dc.identifier.otherSCOPUS;2-s2.0-85063039577
dc.identifier.urihttp://hdl.handle.net/10784/29211
dc.language.isoengeng
dc.publisherElsevier BV
dc.publisher.departmentUniversidad EAFIT. Departamento de Ingeniería Mecánicaspa
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S0167473018303266?dgcid=author
dc.rightshttps://v2.sherpa.ac.uk/id/publication/issn/0167-4730
dc.sourceSTRUCTURAL SAFETY
dc.subject.keywordBayesian networkseng
dc.subject.keywordCompressed sensingeng
dc.subject.keywordGaussian distributioneng
dc.subject.keywordGaussian noise (electronic)eng
dc.subject.keywordStochastic modelseng
dc.subject.keywordStochastic systemseng
dc.subject.keywordTime serieseng
dc.subject.keywordUncertainty analysiseng
dc.subject.keywordWindeng
dc.subject.keywordBayesian methodseng
dc.subject.keywordCompressive sensingeng
dc.subject.keywordRandom field generatorseng
dc.subject.keywordStochastic simulationseng
dc.subject.keywordWind speed time serieseng
dc.subject.keywordParameter estimationeng
dc.titleSimulation of non-stationary non-Gaussian random fields from sparse measurements using Bayesian compressive sampling and Karhunen-Loève expansioneng
dc.typeinfo:eu-repo/semantics/articleeng
dc.typearticleeng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.typepublishedVersioneng
dc.type.localArtículospa

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