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LEHKÝ, D. SLOWIK, O. NOVÁK, D.
Original Title
Reliability-based design: Artificial neural networks and double-loop reliability based optimization approaches
Type
journal article in Web of Science
Language
English
Original Abstract
Two advanced optimization approaches to solving a reliability-based design problem are presented. The first approach is based on the utilization of an artificial neural network and a small-sample simulation technique. The second approach considers an inverse reliability task as a reliability-based optimization task using a double-loop optimization method based on small-sample simulation. Both techniques utilize Latin hypercube sampling with correlation control. The efficiency of both approaches is tested using three numerical examples of structural design – a cantilever beam, a reinforced concrete slab and a post-tensioned composite bridge. The advantages and disadvantages of the approaches are discussed
Keywords
Inverse reliability problem; Artificial neural network; Double-loop reliability-based optimization; Latin hypercube sampling; First order reliability method
Authors
LEHKÝ, D.; SLOWIK, O.; NOVÁK, D.
Released
1. 3. 2018
ISBN
0965-9978
Periodical
ADVANCES IN ENGINEERING SOFTWARE
Year of study
1
Number
State
United Kingdom of Great Britain and Northern Ireland
Pages from
123
Pages to
135
Pages count
13
URL
https://www.sciencedirect.com/science/article/pii/S0143974X16305454
BibTex
@article{BUT142491, author="David {Lehký} and Ondřej {Slowik} and Drahomír {Novák}", title="Reliability-based design: Artificial neural networks and double-loop reliability based optimization approaches", journal="ADVANCES IN ENGINEERING SOFTWARE", year="2018", volume="1", number="1", pages="123--135", doi="10.1016/j.advengsoft.2017.06.013", issn="0965-9978", url="https://www.sciencedirect.com/science/article/pii/S0143974X16305454" }