Application of artificial neural network on vibration test data for damage identification in bridge girder
Structures are exposed to damage during their service life which can severely affect their safety and functionality. Thus, it is important to monitor structures for the occurrence, location and extent of damage. Artificial neural networks (ANNs) as a numerical technique have been applied increas...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Academic Journals
2011
|
| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/7902/1/J14685_2469f43f7896fd3263d6f66334b901ce.pdf http://eprints.uthm.edu.my/7902/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Universiti Tun Hussein Onn Malaysia |
| Language: | English |
| Summary: | Structures are exposed to damage during their service life which can severely affect their safety and
functionality. Thus, it is important to monitor structures for the occurrence, location and extent of
damage. Artificial neural networks (ANNs) as a numerical technique have been applied increasingly for
damage identification with varied success. ANNs are inspired by human biological neurons and have
been used to model some specific problems in many areas of engineering and science to achieve
reasonable results. ANNs have the ability to learn from examples and then adapt to changing
situations when sufficient input-output data are available. This paper presents the application of ANNs
for detection of damage in a steel girder bridge using natural frequencies as dynamic parameters.
Dynamic parameters are easy to implement for damage assessment and can be directly linked to the
topology of structure. In this study, the required data for the ANNs in the form of natural frequencies
will be obtained from experimental modal analysis. This paper also highlights the concept of ANNs
followed by the detail presentation of the experimental modal analysis for natural frequencies
extraction. |
|---|