Evidence Theory and Differential Evolution for Uncertainty Quantification of Structures

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Due to lack of knowledge or incomplete, inaccurate, unclear information in the modeling, simulation, measurement and reliability assessment and design optimization, there are limitations in using only one framework (probability theory) to quantify the uncertainty in a system because of the impreciseness of data or knowledge. In this paper, evidence theory is proposed as an alternative to the classical probability theory to handle the imprecise data situation. In order to alleviate the computational difficulties in the evidence theory based uncertainty quantification (UQ) analysis, a differential evolution based interval optimization for computing bounds method is developed. A typical truss structure with the aleatory and epistemic uncertainties is investigated to demonstrate accuracy and efficiency of the proposed method.

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Edited by:

Xiong Zhou and Honghua Tan

Pages:

1112-1118

Citation:

L. X. Deng et al., "Evidence Theory and Differential Evolution for Uncertainty Quantification of Structures", Applied Mechanics and Materials, Vols. 249-250, pp. 1112-1118, 2013

Online since:

December 2012

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$38.00

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