[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.12989/sss.2014.14.2.159

Modal parameters based structural damage detection using artificial neural networks - a review

Hakim, S.J.S. (StrucHMRS Group, Department of Civil Engineering, University of Malaya)
Razak, H. Abdul (StrucHMRS Group, Department of Civil Engineering, University of Malaya)

Publication Information

Smart Structures and Systems / v.14, no.2, 2014 , pp. 159-189 More about this Journal

Abstract

One of the most important requirements in the evaluation of existing structural systems and ensuring a safe performance during their service life is damage assessment. Damage can be defined as a weakening of the structure that adversely affects its current or future performance which may cause undesirable displacements, stresses or vibrations to the structure. The mass and stiffness of a structure will change due to the damage, which in turn changes the measured dynamic response of the system. Damage detection can increase safety, reduce maintenance costs and increase serviceability of the structures. Artificial Neural Networks (ANNs) are simplified models of the human brain and evolved as one of the most useful mathematical concepts used in almost all branches of science and engineering. ANNs have been applied increasingly due to its powerful computational and excellent pattern recognition ability for detecting damage in structural engineering. This paper presents and reviews the technical literature for past two decades on structural damage detection using ANNs with modal parameters such as natural frequencies and mode shapes as inputs.

Keywords

Artificial Neural Networks (ANNs); Finite Element Analysis (FEA); Back Propagation Neural Network (BPNN); Multi-Layer Perceptron (MLP);

Citations & Related Records

Times Cited By KSCI : 9 (Citation Analysis)

Reference
Cited By KSCI

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