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A data-driven approach to non-parametric reliability-based design optimization of structures with uncertain load

  • Yoshihiro KannoEmail author
Research Paper
  • 56 Downloads

Abstract

This paper presents a simple method for conservatively solving a reliability-based design optimization (RBDO) problem of structures, when only a set of random samples of uncertain parameters is available. Specifically, we consider the truss design optimization under the stress constraints, where the external load is a random vector. The target confidence level, i.e., the probability that the structural reliability is no smaller than the target reliability, is specified, without any assumption on statistical information of the input distribution. We formulate a robust design optimization problem, any feasible solution of which satisfies the reliability constraint with the specified confidence level. The derived robust design optimization problem is solved with a sequential semidefinite programming. Two numerical examples are solved to show the trade-off between the specified confidence level and the structural volume.

Keywords

Reliability-based design optimization Data-driven approach Uncertain input distribution Reliability with confidence Order statistics Robust optimization 

Notes

Acknowledgments

This work is partially supported by JSPS KAKENHI 17K06633.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Mathematics and Informatics CenterThe University of TokyoTokyoJapan

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