Detail publikačního výsledku

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.

Originální název

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

Anglický název

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

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 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.

Anglický abstrakt

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.

Klíčová slova

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

Klíčová slova v angličtině

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

Autoři

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

Rok RIV

2025

Vydáno

01.05.2024

Nakladatel

SPRINGER INTERNATIONAL PUBLISHING AG

Místo

CHAM

ISBN

978-3-031-60271-9

Kniha

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

Svazek

494

Strany od

366

Strany do

376

Strany počet

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"
}