2016
DOI: 10.1177/1687814016671447
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Structural reliability analysis of multiple limit state functions using multi-input multi-output support vector machine

Abstract: Selecting and using an appropriate structural reliability method is critical for the success of structural reliability analysis and reliability-based design optimization. However, most of existing structural reliability methods are developed and designed for a single limit state function and few methods can be used to simultaneously handle multiple limit state functions in a structural system when the failure probability of each limit state function is of interest, for example, in a reliability-based design op… Show more

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Cited by 19 publications
(5 citation statements)
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“…Various DOE schemes have been developed for efficient reliability estimation by SVR-based metamodeling approach. Li et al [73] adopted the median Latin hypercube (LH) sampling to obtain the training samples for the multi-input multi-output SVR model which was used for SRA of problems involving multiple LSFs. Further, Li et al [68] proposed a hybrid approach combining uniform design and SVR-based metamodel for reliability analysis of tunnel structures.…”
Section: Doe Schemes For Svr-based Reliability Analysismentioning
confidence: 99%
“…Various DOE schemes have been developed for efficient reliability estimation by SVR-based metamodeling approach. Li et al [73] adopted the median Latin hypercube (LH) sampling to obtain the training samples for the multi-input multi-output SVR model which was used for SRA of problems involving multiple LSFs. Further, Li et al [68] proposed a hybrid approach combining uniform design and SVR-based metamodel for reliability analysis of tunnel structures.…”
Section: Doe Schemes For Svr-based Reliability Analysismentioning
confidence: 99%
“…Kernel‐based surrogate modeling methods typically make use of local information related to each training data point and combine this information to define the overall surrogate model. Support vector regression, neural network, and Kriging (also known as Gaussian process modeling) are popular kernel‐based surrogate modeling techniques. Recently, Schöbi et al and Kersaudy et al have developed a new surrogate modeling technique called PC‐Kriging.…”
Section: Introductionmentioning
confidence: 99%
“…The methods mentioned above are all available in the literature. [9][10][11][12][13][14][15] Corrosion of underground pipeline is associated with high degree of variability and has been a major contributing factor for the pipe failure as a result of large spatial and temporary variabilities. 16 Due to the spatial and temporary variabilities of the corrosion parameters, the need to develop a more efficient and reliable quantification approach for estimating pipe failure becomes very imperative due to the need to have robust engineering structures.…”
Section: Introductionmentioning
confidence: 99%