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A comparison of damage detection methods applied to civil engineering structures
Facilitating detection of early-stage damage is crucial for in-time repairs and cost-optimized maintenance plans of civil engineering structures. Preferably, the damage detection is performed by use of output vibration data, hereby avoiding modal identification of the structure. Most of the work within the vibration-based damage detection research field assumes that the unmeasured excitation signal is time-invariant with a constant covariance, which is hardly achieved in practice. In this paper, we present a comparison of a new Mahalanobis distance-based damage detection method with the well-known subspace-based damage detection algorithm robust to changes in the excitation covariance. Both methods are implemented in the modal analysis and structural health monitoring software ARTeMIS, in which the joint features of the methods are concluded in a control chart in an attempt to enhance the damage detection resolution. The performances of the methods and their fusion are evaluated in the context of ambient vibration signals obtained from, respectively, numerical simulations on a simple chain-like system and a full-scale experimental example, namely, the Dogna Bridge. The results reveal that the performances of the two damage detection methods are quite similar, hereby evidencing the justification of the new Mahalanobis distance-based approach as it is less computational complex. The control chart presents a comprehensive overview of the progressively damaged structure.
A comparison of damage detection methods applied to civil engineering structures
Facilitating detection of early-stage damage is crucial for in-time repairs and cost-optimized maintenance plans of civil engineering structures. Preferably, the damage detection is performed by use of output vibration data, hereby avoiding modal identification of the structure. Most of the work within the vibration-based damage detection research field assumes that the unmeasured excitation signal is time-invariant with a constant covariance, which is hardly achieved in practice. In this paper, we present a comparison of a new Mahalanobis distance-based damage detection method with the well-known subspace-based damage detection algorithm robust to changes in the excitation covariance. Both methods are implemented in the modal analysis and structural health monitoring software ARTeMIS, in which the joint features of the methods are concluded in a control chart in an attempt to enhance the damage detection resolution. The performances of the methods and their fusion are evaluated in the context of ambient vibration signals obtained from, respectively, numerical simulations on a simple chain-like system and a full-scale experimental example, namely, the Dogna Bridge. The results reveal that the performances of the two damage detection methods are quite similar, hereby evidencing the justification of the new Mahalanobis distance-based approach as it is less computational complex. The control chart presents a comprehensive overview of the progressively damaged structure.
A comparison of damage detection methods applied to civil engineering structures
Gres, Szymon (author) / Andersen, Palle (author) / Johansen, Rasmus Johan (author) / Ulriksen, Martin Dalgaard (author) / Damkilde, Lars (author) / Conte, Joel P. / Astroza, Rodrigo / Benzoni, Gianmario / Feltrin, Glauco / Loh, Kenneth J.
2018-01-01
Gres , S , Andersen , P , Johansen , R J , Ulriksen , M D & Damkilde , L 2018 , A comparison of damage detection methods applied to civil engineering structures . in J P Conte , R Astroza , G Benzoni , G Feltrin , K J Loh & B Moaveni (eds) , Experimental Vibration Analysis for Civil Engineering Structures : Testing, Sensing, Monitoring, and Control . Springer , Cham, Switzerland , Lecture Notes in Civil Engineering , vol. 5 , Lecture Notes in Civil Engineering (electronic) , vol. 5 , pp. 306-316 , 7th International Conference of Experimental Vibration Analysis for Civil Engineering Structures , San Diego, California , United States , 12/07/2017 . https://doi.org/10.1007/978-3-319-67443-8_26
Article (Journal)
Electronic Resource
English
DDC:
624
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