2019
DOI: 10.1016/j.compstruct.2018.11.015
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Uncertainty analysis of composite laminated plate with data-driven polynomial chaos expansion method under insufficient input data of uncertain parameters

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Cited by 35 publications
(16 citation statements)
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“…The local and total sensitivity indices of all input variables can be calculated analytically using Eqs. (25) and (26), which are 0.2208 and 0.2808 respectively. The sensitivity indices are also calculated using the proposed algorithm and the MCS method, and the results are listed in Table 3.…”
Section: B Example 2: Sobol Functionmentioning
confidence: 99%
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“…The local and total sensitivity indices of all input variables can be calculated analytically using Eqs. (25) and (26), which are 0.2208 and 0.2808 respectively. The sensitivity indices are also calculated using the proposed algorithm and the MCS method, and the results are listed in Table 3.…”
Section: B Example 2: Sobol Functionmentioning
confidence: 99%
“…Therefore, metamodel techniques are performed to replace Monte Carlo simulation. Polynomial chaos expansion (PCE) has been recognized as an efficient tool in the uncertainty design with aleatory uncertainty, and then extended to include interval variable [24], [25] or fuzzy variables. Therefore, many metamodels for SA are constructed using PCE method, as originally shown in [26], [27].…”
Section: Introductionmentioning
confidence: 99%
“…So far, the uncertainty analysis of synthetic fiber composites has been investigated by a number of researchers, and various methods have been developed. Probabilistic uncertainty analysis methods, such as traditional Mont Carlo simulation (MCS) methods, stochastic finite element, and response surface methodology (RSM) have been widely applied in the analysis and prediction of uncertain response of composite structures [8][9][10][11][12][13][14].…”
Section: Introductionmentioning
confidence: 99%
“…Instead, surrogate model approaches, which are constructed based on a limited set of actual input/output data points, are a suitable method when dealing with such complex problems. Several methods of application of surrogate models have been reported in the literature for evaluation of uncertainties in composite laminates, such as Kriging method [15,16], radial basis function [17], polynomial chaos expansion (PCE) [18][19][20], and artificial neural network (ANN) [21,22]. State-of-the-art reviews on the surrogate models for evaluating the uncertainty in structural responses of composite laminates can be found in [23].…”
Section: Introductionmentioning
confidence: 99%
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