Paper Titles

Smart Materials in Damage Detection and Prognosis
p.3

Damage Assessment of the Z24 Bridge by FE Model Updating
p.19

An Automatic Damage Detection Methodology for Structural Health Monitoring during Fatigue Tests
p.27

Damage Detection in Large-Scale Laboratory Bridge Models
p.35

Lifetime Prediction for Fatigue Damage in Bonded Joints
p.43

Leak Monitoring in Pipeline Networks Using Wavelet Analysis
p.51

Damage Detection in Boundary Conditions. Comparison Benchmark
p.59

The Frequency Domain Assurance Criterion as a Tool for Damage Detection
p.69

HomeKey Engineering MaterialsKey Engineering Materials Vols. 245-246Damage Detection in Large-Scale Laboratory Bridge...

Damage Detection in Large-Scale Laboratory Bridge Models

Article Preview

Abstract:

You might also be interested in these eBooks

Damage Assessment of Structures V

Info:

Periodical:

Key Engineering Materials (Volumes 245-246)

Pages:

35-42

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.245-246.35

Citation:

Cite this paper

Online since:

July 2003

Authors:

J.S. Owen, N. Haritos

Keywords:

Bridge, Finite Element, Structural Dynamics, Structural Health Monitoring (SHM)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

© 2003 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] N Haritos & J S Owen, The Use of Vibration Data for Damage Detection in Bridges: A Comparison of System Identification and Pattern Recognition Approaches., International Journal of Structural Health Monitoring (under review).

DOI: 10.1177/1475921704042698

[2] I Trendafilova, Pattern recognition methods for damage diagnosis in structures from vibration measurements., Key Engineering Materials Vols 204-205, (2001) pp.85-94.

DOI: 10.4028/www.scientific.net/kem.204-205.85

[3] C Zang & M Imregun, Structural Damage detection using artificial neural networks and measured FRF data reduced via principal component projection, Journal of Sound and Vibration, Vol 242(5), (2001), pp.813-0.

DOI: 10.1006/jsvi.2000.3390

1.

2.

3.

4.

5.

6.

7.

8.

9 1 0 5 10 15 20 25 Damage Level ANN Output Output 1 Output 2 0.

1.

2.

3.

4.

5.

6.

7.

8.

9 1 0 5 10 15 20 25 Damage Level ANN Output Output 1 Output 2 x 1 x 2 x 10 h 1 h 2 h 10 o 2 o 1 o 1 o 1.