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Robust topology optimization of structures under uncertain propagation of imprecise stochastic-based uncertain field

Gao, Kang, Do, Duy Minh, Chu, Sheng, Wu, Gang, Kim, H. Alicia and Featherston, Carol A. 2022. Robust topology optimization of structures under uncertain propagation of imprecise stochastic-based uncertain field. Thin-Walled Structures 175 , 109238. 10.1016/j.tws.2022.109238

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Abstract

This study introduces a novel computational framework for Robust Topology Optimization (RTO) considering imprecise random field parameters. Unlike the worst-case approach, the present method provides upper and lower bounds for the mean and standard deviation of compliance as well as the optimized topological layouts of a structure for various scenarios. In the proposed approach, the imprecise random field variables are determined utilizing parameterized p-boxes with different confidence intervals. The Karhunen–Loève (K–L) expansion is extended to provide a spectral description of the imprecise random field. The linear superposition method in conjunction with a linear combination of orthogonal functions is employed to obtain explicit mathematical expressions for the first and second order statistical moments of the structural compliance. Then, an interval sensitivity analysis is carried out, applying the Orthogonal Similarity Transformation (OST) method with the boundaries of each of the intermediate variable searched efficiently at every iteration using a Combinatorial Approach (CA). Finally, the validity, accuracy, and applicability of the work are rigorously checked by comparing the outputs of the proposed approach with those obtained using the particle swarm optimization (PSO) and Quasi-Monte-Carlo Simulation (QMCS) methods. Three different numerical examples with imprecise random field loads are presented to show the effectiveness and feasibility of the study.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Additional Information: This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
Publisher: Elsevier
ISSN: 0263-8231
Date of First Compliant Deposit: 28 April 2022
Date of Acceptance: 22 March 2022
Last Modified: 28 Apr 2022 11:30
URI: https://orca.cardiff.ac.uk/id/eprint/149362

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