Detail publikace

Reliability-based design: Artificial neural networks and double-loop reliability based optimization approaches

LEHKÝ, D. SLOWIK, O. NOVÁK, D.

Originální název

Reliability-based design: Artificial neural networks and double-loop reliability based optimization approaches

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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

Klíčová slova

Inverse reliability problem; Artificial neural network; Double-loop reliability-based optimization; Latin hypercube sampling; First order reliability method

Autoři

LEHKÝ, D.; SLOWIK, O.; NOVÁK, D.

Vydáno

1. 3. 2018

ISSN

0965-9978

Periodikum

ADVANCES IN ENGINEERING SOFTWARE

Ročník

1

Číslo

1

Stát

Spojené království Velké Británie a Severního Irska

Strany od

123

Strany do

135

Strany počet

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"
}