Publication detail

Damage detection of riveted truss bridge using ANN-aided AMS optimization method

ŠPLÍCHAL, B., LEHKÝ, D., LAMPEROVÁ, K.

Original Title

Damage detection of riveted truss bridge using ANN-aided AMS optimization method

Type

conference paper

Language

English

Original Abstract

Aging transport infrastructure brings increased economic burden and uncertainties regarding the reliability, durability and safe use of structures. Early damage detection to locate incipient damage provides an opportunity for early structural maintenance and can guarantee structural reliability and continuing serviceability. This paper describes the use of the hybrid identification method, which combines a metaheuristic optimization technique aimed multilevel sampling with an artificial neural network-based surrogate model to approximate the inverse relationship between structural response and structural parameters. The method is applied to identify damage in existing riveted truss bridge. The effect of the damage rate and location on the identification speed and the accuracy of the solution is investigated and discussed

Keywords

Damage identification; Model updating; Artificial neural network; Optimization method

Authors

ŠPLÍCHAL, B., LEHKÝ, D., LAMPEROVÁ, K.

Released

12. 7. 2024

Publisher

CRC Press

Location

London

ISBN

9781003483755

Book

Bridge Maintenance, Safety, Management, Digitalization and Sustainability

Edition

1st Edition

Pages from

2279

Pages to

2286

Pages count

8

URL

https://www.taylorfrancis.com/books/oa-edit/10.1201/9781003483755

BibTex

@inproceedings{BUT188906,
  author="Bohumil {Šplíchal} and David {Lehký} and Katarína {Lamperová}",
  title="Damage detection of riveted truss bridge using ANN-aided AMS optimization method",
  booktitle="Bridge Maintenance, Safety, Management, Digitalization and Sustainability",
  year="2024",
  series="1st Edition",
  pages="2279--2286",
  publisher="CRC Press",
  address="London",
  doi="10.1201/9781003483755-270",
  isbn="9781003483755",
  url="https://www.taylorfrancis.com/books/oa-edit/10.1201/9781003483755"
}