Intelligent-based Structural Damage Detection Model
- Department of Building and Construction, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong (Hong Kong)
This paper 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.
- OSTI ID:
- 21361979
- Journal Information:
- AIP Conference Proceedings, Vol. 1233, Issue 1; Conference: 2. international symposium on computational mechanics; 12. international conference on the enhancement and promotion of computational methods in engineering and science, Hong Kong (Hong Kong); Hong Kong (Hong Kong), 30 Nov - 3 Dec 2009; 30 Nov - 3 Dec 2009; Other Information: DOI: 10.1063/1.3452227; (c) 2010 American Institute of Physics; ISSN 0094-243X
- Country of Publication:
- United States
- Language:
- English
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