Neural network based damage detection using substructure technique

Bakhary, Norhisham, Hao, Hong and Deeks, Andrew J. (2007). Neural network based damage detection using substructure technique. In: Martin Veidt, Faris Albermani, Bill Daniel, John Griffiths, Doug Hargreaves, Ross McAree, Paul Meehan and Andy Tan, Proceedings of the 5th Australasian Congress on Applied Mechanics (ACAM 2007). 5th Australasian Congress on Applied Mechanics (ACAM 2007), Brisbane, Australia, (204-214). 10-12 December, 2007.

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Author Bakhary, Norhisham
Hao, Hong
Deeks, Andrew J.
Title of paper Neural network based damage detection using substructure technique
Conference name 5th Australasian Congress on Applied Mechanics (ACAM 2007)
Conference location Brisbane, Australia
Conference dates 10-12 December, 2007
Proceedings title Proceedings of the 5th Australasian Congress on Applied Mechanics (ACAM 2007)
Place of Publication Brisbane, Australia
Publisher Engineers Australia
Publication Year 2007
Year available 2008
Sub-type Fully published paper
ISBN 0 8582 5862 5
Editor Martin Veidt
Faris Albermani
Bill Daniel
John Griffiths
Doug Hargreaves
Ross McAree
Paul Meehan
Andy Tan
Volume 1
Start page 204
End page 214
Total pages 11
Collection year 2007
Language eng
Abstract/Summary Many researchers have been studying the feasibility of using Artificial Neural Networks (ANN) in structural health monitoring and damage detection. It has been proven by both numerical simulation and laboratory test data that ANN can give reliable prediction of structural conditions. The main drawback of using ANN in structural condition monitoring is the requirement of enormous computational effort. Consequently almost all the previous work described in the literature limited the structural members to a small number of large elements in the ANN model. This may result in the ANN model being insensitive to local damage, especially when this local damage is small. To overcome this problem, this study presents an approach to detect small structural damage by using ANN progressively. It uses the substructure technique together with a two-stage ANN to detect the location and extent of the damage. It starts by dividing the structure into a few substructures. The condition of each substructure is examined. Those substructures with condition change identified are further subdivided and their condition examined. By doing this progressively, the location and severity of low level structural damage can be detected. Modal parameters such as frequencies and mode shapes are used as the input to the ANN. To demonstrate the effectiveness of this approach, a two-span continuous concrete slab structure is used as an example. Different damage scenarios are introduced by reducing the local stiffness of the selected elements at different locations along the structure. The results show that this technique successfully detects simulated damage in the structure.
Subjects 290501 Mechanical Engineering
0913 Mechanical Engineering
Keyword Artificial Neural Networks (ANN)
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Unknown

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Created: Mon, 10 Mar 2008, 14:25:45 EST by Thelma Whitbourne on behalf of School of Engineering