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Location and Severity Identification of Notch-Type Damage in a Two-Storey Steel Framed Structure Utilising Frequency Response Functions and Artificial Neural Network
This paper presents a vibration-based damage identification method that utilises damage fingerprints embedded in frequency response functions (FRFs) to identify location and severity of notch-type damage in a two-storey framed structure. The proposed method utilises artificial neural networks (ANNs) to map changes in FRFs to damage characteristics. To enhance damage fingerprints in FRF data, residual FRFs, which are differences in FRF data between the undamaged and the damaged structures, are used for ANN inputs. By adopting principal component analysis (PCA) techniques, the size of the residual FRF data is reduced in order to obtain suitable patterns for ANN inputs. A hierarchy of neural network ensembles is created to take advantage of individual characteristics of measurements from different locations. The method is applied to laboratory and numerical two-storey framed structures. A number of single notch-type damage scenarios of different locations and severities are investigated. To simulate field-testing conditions, numerically simulated data is polluted with white Gaussian noise of up to 10% noise-to-signal-ratio. The results from both numerical and experimental investigations show the proposed method is effective and robust for detecting notch-type damage in structures.
Location and Severity Identification of Notch-Type Damage in a Two-Storey Steel Framed Structure Utilising Frequency Response Functions and Artificial Neural Network
This paper presents a vibration-based damage identification method that utilises damage fingerprints embedded in frequency response functions (FRFs) to identify location and severity of notch-type damage in a two-storey framed structure. The proposed method utilises artificial neural networks (ANNs) to map changes in FRFs to damage characteristics. To enhance damage fingerprints in FRF data, residual FRFs, which are differences in FRF data between the undamaged and the damaged structures, are used for ANN inputs. By adopting principal component analysis (PCA) techniques, the size of the residual FRF data is reduced in order to obtain suitable patterns for ANN inputs. A hierarchy of neural network ensembles is created to take advantage of individual characteristics of measurements from different locations. The method is applied to laboratory and numerical two-storey framed structures. A number of single notch-type damage scenarios of different locations and severities are investigated. To simulate field-testing conditions, numerically simulated data is polluted with white Gaussian noise of up to 10% noise-to-signal-ratio. The results from both numerical and experimental investigations show the proposed method is effective and robust for detecting notch-type damage in structures.
Location and Severity Identification of Notch-Type Damage in a Two-Storey Steel Framed Structure Utilising Frequency Response Functions and Artificial Neural Network
Samali, Bijan (author) / Dackermann, Ulrike (author) / Li, Jianchun (author)
Advances in Structural Engineering ; 15 ; 743-757
2012-05-01
15 pages
Article (Journal)
Electronic Resource
English
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