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LEHKÝ, D. NOVÁK, D. SLOWIK, O. ŠOMODÍKOVÁ, M. CAO, M.
Original Title
Soft Computing and Stochastic Optimization Approaches for Uncertain Design Parameters Determination of Post-Tensioned Composite Bridge
Type
conference paper
Language
English
Original Abstract
To achieve desired level of reliability in limit state design is generally not an easy task, especially when probabilistic analysis including detailed description of uncertainties is utilized. In general, engineering design belongs to the category of inverse problems with the aim to determine selected design parameters. Inn the paper two alternative approaches are employed for finding design parameters of a single-span post-tensioned composite bridge. The first approach is based on utilization of artificial neural network in combination with small-sample simulation technique and genetic algorithms. The second approach considers inverse problem as reliability-based optimization task using small-sample double-loop method.
Keywords
Reliability-based design, inverse analysis, artificial neural network, double-loop optimization, post-tensioned bridge, reliability index, latin hypercube sampling
Authors
LEHKÝ, D.; NOVÁK, D.; SLOWIK, O.; ŠOMODÍKOVÁ, M.; CAO, M.
Released
28. 5. 2016
Location
Shanghai, China
ISBN
978-7-5608-6303-0
Book
Structural Reliability and its Applications (APSSRA ´6)
Pages from
624
Pages to
629
Pages count
6
BibTex
@inproceedings{BUT128479, author="David {Lehký} and Drahomír {Novák} and Ondřej {Slowik} and Martina {Sadílková Šomodíková} and Maosen {Cao}", title="Soft Computing and Stochastic Optimization Approaches for Uncertain Design Parameters Determination of Post-Tensioned Composite Bridge", booktitle="Structural Reliability and its Applications (APSSRA ´6)", year="2016", pages="624--629", address="Shanghai, China", isbn="978-7-5608-6303-0" }