Machine learning-aided stochastic static analysis of functionally graded porous plates

Feng, Y; Wu, D; Chen, X; Gao, W

Machine learning-aided stochastic static analysis of functionally graded porous plates

Feng, Y Wu, D

Chen, X Gao, W

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Publisher:: Elsevier
Publication Type:: Chapter
Citation:: Machine Learning Aided Analysis, Design, and Additive Manufacturing of Functionally Graded Porous Composite Structures, 2023, pp. 271-292
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Feng, Y
dc.contributor.author	Wu, D https://orcid.org/0000-0002-7284-5024
dc.contributor.author	Chen, X
dc.contributor.author	Gao, W
dc.date.accessioned	2024-05-27T07:19:10Z
dc.date.available	2024-05-27T07:19:10Z
dc.date.issued	2023-01-01
dc.identifier.citation	Machine Learning Aided Analysis, Design, and Additive Manufacturing of Functionally Graded Porous Composite Structures, 2023, pp. 271-292
dc.identifier.isbn	9780443154263
dc.identifier.uri	http://hdl.handle.net/10453/179238
dc.description.abstract	A machine learning-aided stochastic framework is introduced for the static analysis (MLA-SSA) of functionally graded porous (FGP) structures involving uncertainties. The proposed framework solves the random static analysis problems with uncertain systematic inputs. Various types of material properties can be modeled as spatially dependent uncertainties as random fields. To effectively solve the stochastic static problems, a novel machine learning technique, namely the extended support vector regression is introduced, along with a typical algorithm named the neural network as comparison. Through the MLA-SSA framework, the statistical moments, the probability density function and the cumulative density function of random deflections can be acquired in an efficient manner. Detailed numerical investigations have been implemented to illustrate the accuracy, efficiency, and applicability of the MLA-SSA framework for FGP structures.
dc.language	en
dc.publisher	Elsevier
dc.relation	http://purl.org/au-research/grants/arc/IH150100006
dc.relation	http://purl.org/au-research/grants/arc/DP230102781
dc.relation.ispartof	Machine Learning Aided Analysis, Design, and Additive Manufacturing of Functionally Graded Porous Composite Structures
dc.relation.isbasedon	10.1016/B978-0-443-15425-6.00010-9
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Machine learning-aided stochastic static analysis of functionally graded porous plates
dc.type	Chapter
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2024-05-27T07:19:08Z
pubs.publication-status	Published

Abstract:

A machine learning-aided stochastic framework is introduced for the static analysis (MLA-SSA) of functionally graded porous (FGP) structures involving uncertainties. The proposed framework solves the random static analysis problems with uncertain systematic inputs. Various types of material properties can be modeled as spatially dependent uncertainties as random fields. To effectively solve the stochastic static problems, a novel machine learning technique, namely the extended support vector regression is introduced, along with a typical algorithm named the neural network as comparison. Through the MLA-SSA framework, the statistical moments, the probability density function and the cumulative density function of random deflections can be acquired in an efficient manner. Detailed numerical investigations have been implemented to illustrate the accuracy, efficiency, and applicability of the MLA-SSA framework for FGP structures.

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http://hdl.handle.net/10453/179238