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Performance of surrogate modeling techniques in structural reliability

International Conference on Applications of Statistics and Probability (12th : 2015 : Vancouver, B.C.)

Solution of structural reliability and uncertainty propagation problems by Monte Carlo simulation can be a demanding task, since complex mechanical models usually have to be solved repeated times. Therefore, surrogate models are often required to reduce the computational burden. This article compares the performance of three surrogate modeling techniques in the solution of structural reliability problems. It addresses artificial neural networks, polynomial chaos and kriging meta-modeling, associated with LHS and Monte-Carlo simulation. A simple procedure for mapping input data for uncertainty quantification problems is also proposed.

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This collection contains the proceedings of ICASP12, the 12th International Conference on Applications of Statistics and Probability in Civil Engineering held in Vancouver, Canada on July 12-15, 2015. Abstracts were peer-reviewed and authors of accepted abstracts were invited to submit full papers. Also full papers were peer reviewed. The editor for this collection is Professor Terje Haukaas, Department of Civil Engineering, UBC Vancouver.

Vancouver : University of British Columbia Library

10.14288/1.0076049

Non UBC

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/2.5/ca/