Techno Press

Steel and Composite Structures   Volume 14, Number 4, April 2013, pages 367-377
Structural damage detection of steel bridge girder using artificial neural networks and finite element models
S.J.S. Hakim and H. Abdul Razak

Abstract     [Full Text]
    Damage in structures often leads to failure. Thus it is very important to monitor structures for the occurrence of damage. When damage happens in a structure the consequence is a change in its modal parameters such as natural frequencies and mode shapes. Artificial Neural Networks (ANNs) are inspired by human biological neurons and have been applied for damage identification with varied success. Natural frequencies of a structure have a strong effect on damage and are applied as effective input parameters used to train the ANN in this study. The applicability of ANNs as a powerful tool for predicting the severity of damage in a model steel girder bridge is examined in this study. The data required for the ANNs which are in the form of natural frequencies were obtained from numerical modal analysis. By incorporating the training data, ANNs are capable of producing outputs in terms of damage severity using the first five natural frequencies. It has been demonstrated that an ANN trained only with natural frequency data can determine the severity of damage with a 6.8% error. The results shows that ANNs trained with numerically obtained samples have a strong potential for structural damage identification.
Key Words
    artificial neural networks (ANNs); finite element; damage detection; backpropagation algorithm; natural frequency
S.J.S. Hakim and H. Abdul Razak: StrucHMRS Group, Department of Civil Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia

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