Intelligent-based Structural Damage Detection Model

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

14 Scopus Citations
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Original languageEnglish
Pages (from-to)590-596
Journal / PublicationMechanics of Advanced Materials and Structures
Issue number8
Online published16 Nov 2011
Publication statusPublished - Dec 2011


This article presents the application of a novel artificial neural network (ANN) model for the diagnosis of structural damage. The ANN model, denoted as the GRNNFA, is a hybrid model combining the general regression neural network model (GRNN) and the fuzzy ART (FA) model. It not only retains the important features of the GRNN and FA models (i.e., fast and stable network training and incremental growth of network structure) but also facilitates the removal of the noise embedded in the training samples. Structural damage alters the stiffness distribution of the structure and so as to change the natural frequencies and mode shapes of the system. The measured modal parameter changes due to a particular damage are treated as patterns for that damage. The proposed GRNNFA model was trained to learn those patterns in order to detect the possible damage location of the structure. Simulated data is employed to verify and illustrate the procedures of the proposed ANN-based damage diagnosis methodology. The results of this study have demonstrated the feasibility of applying the GRNNFA model to structural damage diagnosis even when the training samples were noise contaminated.

Research Area(s)

  • artificial neural network, damage detection, GRNNFA