Structural Damage Detection by Wavelet Transform and Probabilistic Neural Network
Artificial Neural Networks (ANNs) Have Been Applied in Structural Damage Detection as a Classifier, But Generally a Capable ANNs Has to Be Trained with a Certain Amount of Samples. When Both Damage Locations and Damage Extents Are to Be Identified, the Amount of Training Samples is Tremendous Because of the Combinations of Damage Locations and Extents. by Wavelet Transform of the Structure Free Motion Equations, the Residual Wavelet Coefficient Vector (RWCV) is Deduced. a Damage Feature Parameter is Defined as the Ratio between RWCVs in Two Different Frequency Bands. This Parameter Has a Unique Property that It's Sensitive Only to Damage Locations, and is Independent of Damage Extents. the Damage Feature Parameters Are Then Fed to the Neural Network for Damage Localization. after the Damage Sites Are Detected, the Damage Extent is Further Identified by Another Neural Network with RWCVs as Inputs. This Two-Phase Approach for Damage Localization and Extent Identification Can Simply the Neural Network and Reduce the Training Samples Tremendously. Finally a Numerical Example is Given for Damage Detection of a 10 DOFs System using the Proposed Approach.
G. Yan et al., "Structural Damage Detection by Wavelet Transform and Probabilistic Neural Network," Proceedings of SPIE - The International Society for Optical Engineering, vol. 5765, no. PART 2, pp. 892 - 900, article no. 100, Society of Photo-optical Instrumentation Engineers, Sep 2005.
The definitive version is available at https://doi.org/10.1117/12.602012
Civil, Architectural and Environmental Engineering
Keywords and Phrases
Neural networks; Residual wavelet coefficient vector; Structural damage detection; Wavelet transform
International Standard Serial Number (ISSN)
Article - Conference proceedings
© 2023 Society of Photo-optical Instrumentation Engineers, All rights reserved.
29 Sep 2005