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Statistical methods for damage detection applied to civil structures

Abstract : Damage detection consists of monitoring the deviations of a current system from its reference state, characterized by some nominal property repeatable for every healthy state. Preferably, the damage detection is performed directly on vibration data, hereby avoiding modal identification of the structure. The practical aspect of using only the output measurements cause difficulties because of variations in ambient excitation due to variability in the environmental conditions, like sea, wind, and temperature. In this paper, a new Mahalanobis distance-based damage detection method is studied and compared to the well-known subspace-based damage detection algorithm in the context of two large case studies. 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 resolution of the damage detection. The damage indicators from both methods are evaluated based on the ambient vibration signals from numerical simulations on a novel offshore support structure and experimental example of a full scale bridge. The results reveal that the performance of the two damage detection methods is similar, hereby implying merit of the new Mahalanobis distance-based approach, as it is less computational complex. The fusion of the damage indicators in the control chart provides the most accurate view on the progressively damaged systems.
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Contributor : Michael Döhler Connect in order to contact the contributor
Submitted on : Thursday, October 19, 2017 - 2:04:07 PM
Last modification on : Friday, June 17, 2022 - 1:27:42 PM
Long-term archiving on: : Saturday, January 20, 2018 - 1:23:40 PM


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Szymon Gres, Martin Dalgaard Ulriksen, Michael Döhler, Rasmus Johan Johansen, Palle Andersen, et al.. Statistical methods for damage detection applied to civil structures. EURODYN 2017 - 10th International Conference on Structural Dynamics, Sep 2017, Rome, Italy. pp.1919 - 1924, ⟨10.1016/j.proeng.2017.09.280⟩. ⟨hal-01619442⟩



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