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Vibration‐based structural condition assessment using convolution neural networks
Ist Teil von
Structural control and health monitoring, 2019-02, Vol.26 (2), p.e2308-n/a
Ort / Verlag
Pavia: Wiley Subscription Services, Inc
Erscheinungsjahr
2019
Quelle
Wiley-Blackwell Journals
Beschreibungen/Notizen
Summary
A novel vibration‐based structural health monitoring (SHM) approach that uses two‐dimensional deep convolution neural networks (CNN) is introduced. The CNN extracts the features from acceleration response histories and drastically reduces the dimension of response history to make damage state classification possible with limited number of acceleration measurements. The proposed method was validated, and its applicability and efficiency were demonstrated using vibration response data recorded during the shake‐table testing of a one‐fourth–scale model of a reinforced concrete highway bridge. The proposed method predicted predefined damage states with 100% accuracy using recorded (acceleration) vibration response data. The method was shown to be robust and sensitive to very small changes in structural condition. It is also noted that the CNN‐based SHM method is scalable to any large number of damage states (including extent and location) with suitable network training. The required training data may be generated analytically using a nonlinear finite element model.