Detail publikace

Soft Computing and Stochastic Optimization Approaches for Uncertain Design Parameters Determination of Post-Tensioned Composite Bridge

Originální název

Soft Computing and Stochastic Optimization Approaches for Uncertain Design Parameters Determination of Post-Tensioned Composite Bridge

Anglický název

Soft Computing and Stochastic Optimization Approaches for Uncertain Design Parameters Determination of Post-Tensioned Composite Bridge

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

BibTex


@inproceedings{BUT128479,
  author="David {Lehký} and Drahomír {Novák} and Ondřej {Slowik} and Martina {Šomodíková} and Maosen {Cao}",
  title="Soft Computing and Stochastic Optimization Approaches for Uncertain Design Parameters Determination of Post-Tensioned Composite Bridge",
  annote="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.",
  booktitle="Structural Reliability and its Applications (APSSRA ´6)",
  chapter="128479",
  howpublished="electronic, physical medium",
  year="2016",
  month="may",
  pages="624--629",
  type="conference paper"
}