Publication result detail

Exploration of Adaptive Sequential Sampling in the Definition of Surrogate Models for the Rare Event Estimation in Transportation Assets

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

Original Title

Exploration of Adaptive Sequential Sampling in the Definition of Surrogate Models for the Rare Event Estimation in Transportation Assets

English Title

Exploration of Adaptive Sequential Sampling in the Definition of Surrogate Models for the Rare Event Estimation in Transportation Assets

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 the selection of possible future candidates in the input domain (i.e., random variables). In the present research, different methodologies for obtaining the training sample for a surrogate model were explored, considering sample size, distribution of the points, and identification of the failure region. The effects on the reliability of the slope stability under vertical loading based on the safety factors from Bishop's simplified method were obtained. The results reinforce the importance of the characteristics of the training sample used for the application of surrogate models to describe limit states and their accuracy when employed for the computation of the reliability index.

English abstract

Adaptive sequential sampling provides a good technique to refine and increase the accuracy of surrogate models, used for reliability analysis, based on the selection of possible future candidates in the input domain (i.e., random variables). In the present research, different methodologies for obtaining the training sample for a surrogate model were explored, considering sample size, distribution of the points, and identification of the failure region. The effects on the reliability of the slope stability under vertical loading based on the safety factors from Bishop's simplified method were obtained. The results reinforce the importance of the characteristics of the training sample used for the application of surrogate models to describe limit states and their accuracy when employed for the computation of the reliability index.

Keywords

Failure surface refinement; Performance of transportation assets; Structure-related parameter uncertainties; Reliability assessment

Key words in English

Failure surface refinement; Performance of transportation assets; Structure-related parameter uncertainties; Reliability assessment

Authors

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

RIV year

2025

Released

01.05.2024

Publisher

SPRINGER INTERNATIONAL PUBLISHING AG

Location

CHAM

ISBN

978-3-031-60271-9

Book

20th International Probabilistic Workshop IPW 2024, Lecture Notes in Civil Engineering 494

Volume

494

Pages from

366

Pages to

376

Pages count

11

BibTex

@inproceedings{BUT194147,
  author="Carlos Andres Mendoza {Cabanzo} and Edward {Baron} and Miroslav {Vořechovský} and M. {Akiyama} and Paulo B. {Lourenco} and Jose Campos {Matos}",
  title="Exploration of Adaptive Sequential Sampling in the Definition of Surrogate Models for the Rare Event Estimation in Transportation Assets",
  booktitle="20th International Probabilistic Workshop IPW 2024, Lecture Notes in Civil Engineering 494",
  year="2024",
  volume="494",
  pages="366--376",
  publisher="SPRINGER INTERNATIONAL PUBLISHING AG",
  address="CHAM",
  doi="10.1007/978-3-031-60271-9\{_}34",
  isbn="978-3-031-60271-9"
}