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A hybrid proper orthogonal decomposition-heteroscedastic sparse Gaussian process regression model for evaluating structural reliability with correlated stochastic material properties
https://orcid.org/0000-0001-6243-4304
Highlights A hybrid POD-HSGPR method is proposed for data-driven stochastic material modeling. Both material behaviors and associated uncertainties can be predicted by the model. Stochastic correlations between the material behaviors can be retained in the model.
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.
A hybrid proper orthogonal decomposition-heteroscedastic sparse Gaussian process regression model for evaluating structural reliability with correlated stochastic material properties
https://orcid.org/0000-0001-6243-4304
Highlights A hybrid POD-HSGPR method is proposed for data-driven stochastic material modeling. Both material behaviors and associated uncertainties can be predicted by the model. Stochastic correlations between the material behaviors can be retained in the model.
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.
A hybrid proper orthogonal decomposition-heteroscedastic sparse Gaussian process regression model for evaluating structural reliability with correlated stochastic material properties
https://orcid.org/0000-0001-6243-4304
Highlights A hybrid POD-HSGPR method is proposed for data-driven stochastic material modeling. Both material behaviors and associated uncertainties can be predicted by the model. Stochastic correlations between the material behaviors can be retained in the model.
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.
A hybrid proper orthogonal decomposition-heteroscedastic sparse Gaussian process regression model for evaluating structural reliability with correlated stochastic material properties
Chen, Baixi (author) / Shen, Luming (author) / Zhang, Hao (author)
Structural Safety ; 100
2022-09-26
Article (Journal)
Electronic Resource
English
Probabilistic Proper Orthogonal Decomposition
| British Library Conference Proceedings | 2010