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Title: Intelligent-based Structural Damage Detection Model

Abstract

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.

Authors:
;  [1]
  1. Department of Building and Construction, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong (Hong Kong)
Publication Date:
OSTI Identifier:
21361979
Resource Type:
Journal Article
Journal Name:
AIP Conference Proceedings
Additional Journal Information:
Journal Volume: 1233; Journal 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; Journal ID: ISSN 0094-243X
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICAL METHODS AND COMPUTING; DAMAGE; DETECTION; DIAGNOSIS; FUZZY LOGIC; NEURAL NETWORKS; NOISE; SIMULATION; TRAINING; EDUCATION; MATHEMATICAL LOGIC

Citation Formats

Lee, Eric Wai Ming, and Yu, K F. Intelligent-based Structural Damage Detection Model. United States: N. p., 2010. Web. doi:10.1063/1.3452227.
Lee, Eric Wai Ming, & Yu, K F. Intelligent-based Structural Damage Detection Model. United States. https://doi.org/10.1063/1.3452227
Lee, Eric Wai Ming, and Yu, K F. 2010. "Intelligent-based Structural Damage Detection Model". United States. https://doi.org/10.1063/1.3452227.
@article{osti_21361979,
title = {Intelligent-based Structural Damage Detection Model},
author = {Lee, Eric Wai Ming and Yu, K F},
abstractNote = {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.},
doi = {10.1063/1.3452227},
url = {https://www.osti.gov/biblio/21361979}, journal = {AIP Conference Proceedings},
issn = {0094-243X},
number = 1,
volume = 1233,
place = {United States},
year = {Fri May 21 00:00:00 EDT 2010},
month = {Fri May 21 00:00:00 EDT 2010}
}