Publication result detail

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.

Original Title

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

English Title

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

Type

Paper in proceedings (conference paper)

Original Abstract

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.

English abstract

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.

Keywords

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

Key words in English

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

Authors

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

Released

09.01.2025

Publisher

SPRINGER INTERNATIONAL PUBLISHING AG

Location

CHAM

ISBN

978-3-031-80724-4

Book

4th fib International Conference on Concrete Sustainability (ICCS2024)

Volume

574

Pages from

516

Pages to

523

Pages count

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"
}