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Contribution to Book
Polynomial Dimensional Decomposition with Adaptive-Sparse Schemes for Stochastic Analysis and Design
Safety, Reliability, Risk, Resilience and Sustainability of Structures and Infrastructure: Proceedings of the International Conference on Structural Safety and Reliability
  • Xuchun Ren, Georgia Southern University
  • Sharif Rahman, University of Iowa
Document Type
Contribution to Book
Publication Date
7-4-2017
ISBN
978-3-903024-28-1
Abstract

This paper explores an accurate and computationally efficient decomposition method, known as the adaptive sparse polynomial dimensional decomposition (AS-PDD), for uncertainty quantification and design under uncertainty of complex engineering systems. Unlike the truncated polynomial dimensional decomposition requiring their truncation parameter(s) to be assigned apriori or arbitrarily, the AS-PDD performs these truncations automatically by progressively drawing in higher-variate or higher-order contributions as appropriate. Two adaptive sparse schemes are explored in this study. The first scheme arises from the variance-based index. The second scheme utilizes the f -index, which is capable of accounting for the entire probability distribution of the output. Shape design of a three-hole bracket with nine random parameters was performed, demonstrating the power of the new method developed to tackle practical robust design optimization problems.

Citation Information
Xuchun Ren and Sharif Rahman. "Polynomial Dimensional Decomposition with Adaptive-Sparse Schemes for Stochastic Analysis and Design" Vienna, AustriaSafety, Reliability, Risk, Resilience and Sustainability of Structures and Infrastructure: Proceedings of the International Conference on Structural Safety and Reliability (2017) p. 1543 - 1552 ISSN: 2523-9198
Available at: http://works.bepress.com/xuchun_ren/19/