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- W4300773905 startingPage "102289" @default.
- W4300773905 abstract "Data-driven material models have shown advantages in recent studies since they can directly use the existing data and improve the model performances further when additional data is available. However, few data-driven material models in the previous studies considered the uncertainty and stochastic correlations of the material properties. In this work, the hybrid Proper Orthogonal Decomposition-Heteroscedastic Sparse Gaussian Process Regression (POD-HSGPR) framework is proposed for predicting stochastic material behaviors and their correlation. The two material behaviors case studies on the metal strength and the rock joint behavior have demonstrated that the proposed POD-HSGPR-based stochastic material model can effectively capture the material properties, material uncertainty, and stochastic correlation between the material behaviors directly from the experimental dataset. The proposed POD-HSGPR model is then applied to a rock slope structure problem to investigate the influence of the correlation of material properties on structural behavior. The results indicate that the proposed POD-HSGPR model could effectively quantify the correlation effect in a non-parametric format and avoid the overestimation of structural reliability." @default.
- W4300773905 created "2022-10-04" @default.
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- W4300773905 date "2023-01-01" @default.
- W4300773905 modified "2023-10-06" @default.
- W4300773905 title "A hybrid proper orthogonal decomposition-heteroscedastic sparse Gaussian process regression model for evaluating structural reliability with correlated stochastic material properties" @default.
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- W4300773905 doi "https://doi.org/10.1016/j.strusafe.2022.102289" @default.
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