NDLI: Classification approach for reliability-based topology optimization using probabilistic neural networks

Content Provider	Springer Nature Link
Author	Patel, Jiten Choi, Seung Kyum
Copyright Year	2011
Abstract	This research explores the usage of classification approaches in order to facilitate the accurate estimation of probabilistic constraints in optimization problems under uncertainty. The efficiency of the proposed framework is achieved with the combination of a conventional topology optimization method and a classification approach- namely, probabilistic neural networks (PNN). Specifically, the implemented framework using PNN is useful in the case of highly nonlinear or disjoint failure domain problems. The effectiveness of the proposed framework is demonstrated with three examples. The first example deals with the estimation of the limit state function in the case of disjoint failure domains. The second example shows the efficacy of the proposed method in the design of stiffest structure through the topology optimization process with the consideration of random field inputs and disjoint failure phenomenon, such as buckling. The third example demonstrates the applicability of the proposed method in a practical engineering problem.
Starting Page	529
Ending Page	543
Page Count	15
File Format	PDF
ISSN	1615147X
Journal	Structural and Multidisciplinary Optimization
Volume Number	45
Issue Number	4
e-ISSN	16151488
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-09-27
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Reliability-based topology optimization Probabilistic neural network Uncertainty Classification approach Computational Mathematics and Numerical Analysis Theoretical and Applied Mechanics Engineering Design
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Computer Graphics and Computer-Aided Design Control and Systems Engineering Computer Science Applications Software

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