Detail publikačního výsledku

Reliability-Based Analysis of a Portuguese Concrete Arch Bridge Employing Surrogate Modeling Techniques Based on Adaptive Sequential Sampling

CABANZO, C.; BARON, E.; VOŘECHOVSKÝ, M.; AKIYAMA, M.; LAURENCO, P.; MATOS, J.

Originální název

Reliability-Based Analysis of a Portuguese Concrete Arch Bridge Employing Surrogate Modeling Techniques Based on Adaptive Sequential Sampling

Anglický název

Reliability-Based Analysis of a Portuguese Concrete Arch Bridge Employing Surrogate Modeling Techniques Based on Adaptive Sequential Sampling

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

Adaptive sequential sampling provides a good technique to refine and increase the accuracy of surrogate models used for reliability analysis based on selecting possible future candidates in the input domain (i.e., random variables). In the present research, adaptive sequential sampling was employed to obtain the training experimental design from a set of random variables to develop a surrogate model capable of representing the failure limit of the asset under vertical traffic loads. The surrogate model was then used to obtain the failure probability of the case study, a single-span concrete arch bridge in Portugal. The resulting reliability index was compared with the outcomes obtained in previous research. Finally, by integrating novel sampling approaches a reduction in the computational cost associated with the reliability analysis and an overall better reliability of the surrogate model for evaluating the performance limit state was obtained.

Anglický abstrakt

Adaptive sequential sampling provides a good technique to refine and increase the accuracy of surrogate models used for reliability analysis based on selecting possible future candidates in the input domain (i.e., random variables). In the present research, adaptive sequential sampling was employed to obtain the training experimental design from a set of random variables to develop a surrogate model capable of representing the failure limit of the asset under vertical traffic loads. The surrogate model was then used to obtain the failure probability of the case study, a single-span concrete arch bridge in Portugal. The resulting reliability index was compared with the outcomes obtained in previous research. Finally, by integrating novel sampling approaches a reduction in the computational cost associated with the reliability analysis and an overall better reliability of the surrogate model for evaluating the performance limit state was obtained.

Klíčová slova

Failure probability of roadway assets; Sampling for surrogate modeling; Nonlinear structural analysis; Structural-related parameter uncertainties

Klíčová slova v angličtině

Failure probability of roadway assets; Sampling for surrogate modeling; Nonlinear structural analysis; Structural-related parameter uncertainties

Autoři

CABANZO, C.; BARON, E.; VOŘECHOVSKÝ, M.; AKIYAMA, M.; LAURENCO, P.; MATOS, J.

Vydáno

09.01.2025

Nakladatel

SPRINGER INTERNATIONAL PUBLISHING AG

Místo

CHAM

ISBN

978-3-031-80724-4

Kniha

4th fib International Conference on Concrete Sustainability (ICCS2024)

Svazek

574

Strany od

516

Strany do

523

Strany počet

8

URL

https://link.springer.com/chapter/10.1007/978-3-031-80724-4_63

BibTex

@inproceedings{BUT194172,
  author="Carlos Andres Mendoza {Cabanzo} and Edward {Baron} and Miroslav {Vořechovský} and M. {Akiyama} and Paulo José B. Barbosa {Laurenco} and Jose Campos {Matos}",
  title="Reliability-Based Analysis of a Portuguese Concrete Arch Bridge Employing Surrogate Modeling Techniques Based on Adaptive Sequential Sampling",
  booktitle="4th fib International Conference on Concrete Sustainability (ICCS2024)
",
  year="2025",
  volume="574",
  pages="516--523",
  publisher="SPRINGER INTERNATIONAL PUBLISHING AG",
  address="CHAM",
  doi="10.1007/978-3-031-80724-4\{_}63",
  isbn="978-3-031-80724-4",
  url="https://link.springer.com/chapter/10.1007/978-3-031-80724-4_63"
}