Performance of global metamodeling techniques in solution of structural reliability problems. (December 2017)

• Record Type: Journal Article
• Authors: Kroetz, Henrique M.; Tessari, Rodolfo K.; Beck, André T.
• Is Part Of: Advances in engineering software. Volume 114(2017)
• Page Start: 394
• Page End: 404
• Publication Date: 2017-12
• Subjects: Structural reliability -- Uncertainty quantification -- Metamodels -- Artificial neural networks -- Polynomial chaos expansions -- Kriging; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2017.08.001
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: Metamodeling techniques are applied for solving structural reliability problems. The performance of each technique is studied and compared. Implementation details are discussed. Strategies for overcoming techniques limitations are proposed. Abstract: Solution of structural reliability and uncertainty propagation problems can be a computationally intensive task, since complex mechanical models have to be solved thousands or millions of times. In this context, surrogate models can be employed in order to reduce the computational burden. This article compares the performance of three global surrogate modeling techniques in the solution of structural reliability problems. The paper addresses artificial neural networks, polynomial chaos expansions and Kriging metamodeling. Analytical and numerical problems of increasingly complexity are addressed, including an eight-hundred bar, 3D steel lattice tower. Implementation strategies concerning data mapping and optimization of Kriging hyper parameters are proposed and discussed. Advantages and limitations of each technique are addressed. Results show that the three techniques explored herein are reliable tools for approximating the response of complex mechanical models.