Document Type

Structural health monitoring and damage identification paper

Publication details

Hakim, SJS, Abdul Razak, H, Ravanfar, SA 2014, 'Vibration-based structural damage identification using ensemble neural networks', in ST Smith (ed.), 23rd Australasian Conference on the Mechanics of Structures and Materials (ACMSM23), vol. II, Byron Bay, NSW, 9-12 December, Southern Cross University, Lismore, NSW, pp. 1167-1172. ISBN: 9780994152008.

Peer Reviewed

Peer-Reviewed

Abstract

Damage in structures often leads to failure and can be defined as a weakening of the structure which may cause undesirable displacements, stresses or vibrations to the structure that adversely affects its current or future performance. Thus, it is very important to monitor structures for the occurrence of damage. Reduction in the structural stiffness produces changes in the modal parameters such as the natural frequencies and mode shapes. In this paper, artificial neural networks (ANNs) based damage identification techniques were developed and applied for damage identification in I-beam structures using dynamic parameters. Experimental modal analysis was applied to generate dynamic parameters of the first five flexural modes of structures. In damage identification using ANNs, five individual networks corresponding to mode 1 to mode 5 were trained, and then a method based on neural network ensemble was proposed to combine the outcomes of the individual neural networks into a single network. The ensemble network has the advantages of all the individual networks from different vibrational modes. The results showed that ensemble neural networks have a strong potential for structural damage identification.

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