Detail publikace

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

Originální název

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

Anglický název

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

Jazyk

en

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

Anglický 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

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",
  annote="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",
  chapter="142491",
  doi="10.1016/j.advengsoft.2017.06.013",
  howpublished="online",
  number="1",
  volume="1",
  year="2018",
  month="march",
  pages="123--135",
  type="journal article in Web of Science"
}