Gaussian Process Regression-Based Material Model for Stochastic Structural AnalysisSource: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2021:;Volume ( 007 ):;issue: 003::page 04021025-1DOI: 10.1061/AJRUA6.0001138Publisher: ASCE
Abstract: Data-driven material models can capture the constitutive relationship directly from the data without involving any material-dependent mathematical expressions. But most data-driven approaches, such as artificial neural networks, only estimate the deterministic relations and do not consider the material uncertainty. In this paper, the constitutive relation is taken as a stochastic function following the Gaussian process, where a probability-based nonparametric method, called Gaussian process regression (GPR), is used to capture the constitutive relation with the uncertainty being included. Both one-dimensional (1D) and two-dimensional (2D) material data are used to validate the GPR-based constitutive model (GPR model). The obtained GPR model shows higher accuracy than other data-driven approaches, particularly when the data set size is small. When compared with the assumed true model, the GPR-based model has an average relative error of <2.3%. Finally, with the help of the material uncertainty identified by the GPR-based model from the material data, a data-driven stochastic structural analysis procedure is developed. The relative errors of the expected deflection and probability of failure given by the GPR model are smaller than 2% and 3%, respectively.
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contributor author | Baixi Chen | |
contributor author | Luming Shen | |
contributor author | Hao Zhang | |
date accessioned | 2022-01-31T23:59:27Z | |
date available | 2022-01-31T23:59:27Z | |
date issued | 9/1/2021 | |
identifier other | AJRUA6.0001138.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4270703 | |
description abstract | Data-driven material models can capture the constitutive relationship directly from the data without involving any material-dependent mathematical expressions. But most data-driven approaches, such as artificial neural networks, only estimate the deterministic relations and do not consider the material uncertainty. In this paper, the constitutive relation is taken as a stochastic function following the Gaussian process, where a probability-based nonparametric method, called Gaussian process regression (GPR), is used to capture the constitutive relation with the uncertainty being included. Both one-dimensional (1D) and two-dimensional (2D) material data are used to validate the GPR-based constitutive model (GPR model). The obtained GPR model shows higher accuracy than other data-driven approaches, particularly when the data set size is small. When compared with the assumed true model, the GPR-based model has an average relative error of <2.3%. Finally, with the help of the material uncertainty identified by the GPR-based model from the material data, a data-driven stochastic structural analysis procedure is developed. The relative errors of the expected deflection and probability of failure given by the GPR model are smaller than 2% and 3%, respectively. | |
publisher | ASCE | |
title | Gaussian Process Regression-Based Material Model for Stochastic Structural Analysis | |
type | Journal Paper | |
journal volume | 7 | |
journal issue | 3 | |
journal title | ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering | |
identifier doi | 10.1061/AJRUA6.0001138 | |
journal fristpage | 04021025-1 | |
journal lastpage | 04021025-12 | |
page | 12 | |
tree | ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2021:;Volume ( 007 ):;issue: 003 | |
contenttype | Fulltext |