Detail publikace

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

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

Originální název

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

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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

Klíčová slova

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

Autoři

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

Vydáno

12. 7. 2024

Nakladatel

CRC Press

Místo

London

ISBN

9781003483755

Kniha

Bridge Maintenance, Safety, Management, Digitalization and Sustainability

Edice

1st Edition

Strany od

2279

Strany do

2286

Strany počet

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"
}