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Reliability analysis with Metamodel Line Sampling
HighlightsThe Line Sampling method is coupled with a metamodel of the performance function.Correction coefficient accounts for the metamodel approximation.Metamodel failure probability is updated with the correction coefficient.The estimate of failure probability is asymptotically unbiased.
AbstractThis paper presents an approach for reliability analysis of engineering structures, referred to as Metamodel Line Sampling (MLS). The approach utilizes a metamodel of the performance function, within the framework of the Line Sampling method, to reduce computational demands associated with the reliability analysis of engineering structures. Given a metamodel of the performance function, the failure probability is estimated as a product of a metamodel-based failure probability and a correction coefficient. The correction coefficient accounts for the error in the metamodel estimate of failure probability introduced by the replacement of the performance function with a metamodel. Computational efficiency and accuracy of the MLS approach are evaluated with the Kriging metamodel on analytical reliability problems and a practical reliability problem of a monopile foundation for offshore wind turbine. The MLS approach demonstrated efficient performance in low to medium-dimensional reliability problems.
Reliability analysis with Metamodel Line Sampling
HighlightsThe Line Sampling method is coupled with a metamodel of the performance function.Correction coefficient accounts for the metamodel approximation.Metamodel failure probability is updated with the correction coefficient.The estimate of failure probability is asymptotically unbiased.
AbstractThis paper presents an approach for reliability analysis of engineering structures, referred to as Metamodel Line Sampling (MLS). The approach utilizes a metamodel of the performance function, within the framework of the Line Sampling method, to reduce computational demands associated with the reliability analysis of engineering structures. Given a metamodel of the performance function, the failure probability is estimated as a product of a metamodel-based failure probability and a correction coefficient. The correction coefficient accounts for the error in the metamodel estimate of failure probability introduced by the replacement of the performance function with a metamodel. Computational efficiency and accuracy of the MLS approach are evaluated with the Kriging metamodel on analytical reliability problems and a practical reliability problem of a monopile foundation for offshore wind turbine. The MLS approach demonstrated efficient performance in low to medium-dimensional reliability problems.
Reliability analysis with Metamodel Line Sampling
Depina, Ivan (author) / Le, Thi Minh Hue (author) / Fenton, Gordon (author) / Eiksund, Gudmund (author)
Structural Safety ; 60 ; 1-15
2015-12-27
15 pages
Article (Journal)
Electronic Resource
English
Metamodel , Reliability , Line Sampling , Kriging , Monopile , Offshore
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