Publication detail

Damage Identification Using Artificial Neural Network-Aided Aimed Multilevel Sampling Method

ŠPLÍCHAL, B. LEHKÝ, D

Original Title

Damage Identification Using Artificial Neural Network-Aided Aimed Multilevel Sampling Method

Type

journal article - other

Language

English

Original Abstract

Structural health monitoring is extremely important for sustaining and preserving the service life of civil structures. Research to identify the damage can detect, locate, quantify and, where appropriate, predict potential structural damage. This paper is about damage identified by non-destructive vibrationbased experiments, which uses the difference between modal frequencies and deflection of an initial and damaged structure. The main objective of this paper is to present a hybrid method for structural damage identification combining artificial neural network and aimed multilevel sampling method. The combination of these approaches yields a more efficient damage identification in terms of time and accuracy of damage localization and damage extent determination

Keywords

Damage identification, artificial neural network, aimed multilevel sampling, inverse analysis.

Authors

ŠPLÍCHAL, B.; LEHKÝ, D

Released

31. 12. 2023

Publisher

VSB-Technical University of Ostrava

Location

Ostrava

ISBN

1804-4824

Periodical

Transactions of the VŠB – Technical University of Ostrava, Civil Engineering Series

Year of study

23

Number

2

State

Czech Republic

Pages from

61

Pages to

66

Pages count

6

URL

http://tces.vsb.cz

BibTex

@article{BUT185580,
  author="Bohumil {Šplíchal} and David {Lehký}",
  title="Damage Identification Using Artificial Neural Network-Aided Aimed Multilevel Sampling Method",
  journal="Transactions of the VŠB – Technical University of Ostrava, Civil Engineering Series",
  year="2023",
  volume="23",
  number="2",
  pages="61--66",
  doi="10.35181/tces-2023-0017",
  issn="1804-4824",
  url="http://tces.vsb.cz"
}