Universiti Teknologi Malaysia Institutional Repository

Structure damage detection using neural network with multi-stage substructuring

Bakhary, Norhisham and Hao, H. and Deeks, A. J. (2010) Structure damage detection using neural network with multi-stage substructuring. Advances in Structural Engineering, 13 (1). 95 -110. ISSN 1369-4332

Full text not available from this repository.

Official URL: http://dx.doi.org/10.1260/1369-4332.13.1.95

Abstract

Artificial neural network (ANN) method has been proven feasible by many researchers in detecting damage based on vibration parameters. However, the main drawback of ANN method is the requirement of enormous computational effort especially when complex structures with large degrees of freedom are involved.Consequently, almost all the previous works described in the literature limited the structural members to a small number of large elements in the ANN model which resulted ANN model being insensitive to local damage. This study presents anapproach to detect small structural damage using ANN method with progressive substructure zooming. It uses the substructure technique together with a multi-stage ANN models to detect the location and extent of the damage. Modal parameters suchas frequencies and mode shapes are used as input to ANN. To demonstrate the effectiveness of this approach, a two-span continuous concrete slab structure and a three-storey portal frame are used as examples. Different damage scenarios have been introduced by reducing the local stiffness of the selected elements at different locations in the structures. The results show that this technique successfully detects all the simulated damages in the structure.

Item Type:Article
Uncontrolled Keywords:artificial neural network, concrete slab, neural network
Subjects:T Technology > TA Engineering (General). Civil engineering (General)
Divisions:Civil Engineering
ID Code:26534
Deposited By: Narimah Nawil
Deposited On:18 Jul 2012 09:48
Last Modified:09 Nov 2018 16:09

Repository Staff Only: item control page