Back to Search Start Over

Statistical methods for damage detection applied to civil structures

Authors :
Palle Andersen
Michael Döhler
Szymon Gres
Lars Damkilde
Martin Dalgaard Ulriksen
Søren Andreas Nielsen
Rasmus Johan Johansen
Aalborg University [Denmark] (AAU)
Statistical Inference for Structural Health Monitoring (I4S)
Département Composants et Systèmes (IFSTTAR/COSYS)
PRES Université Lille Nord de France-PRES Université Nantes Angers Le Mans (UNAM)-Université de Lyon-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-PRES Université Lille Nord de France-PRES Université Nantes Angers Le Mans (UNAM)-Université de Lyon-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Inria Rennes – Bretagne Atlantique
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Structural Vibration Solutions (SVIBS)
Universal Foundation A/S
Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Université de Lyon-PRES Université Nantes Angers Le Mans (UNAM)-PRES Université Lille Nord de France-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Université de Lyon-PRES Université Nantes Angers Le Mans (UNAM)-PRES Université Lille Nord de France-Inria Rennes – Bretagne Atlantique
Source :
EURODYN 2017-10th International Conference on Structural Dynamics, EURODYN 2017-10th International Conference on Structural Dynamics, Sep 2017, Rome, Italy. pp.1919-1924, ⟨10.1016/j.proeng.2017.09.280⟩, Gres, S, Ulriksen, M D, Döhler, M, Johansen, R J, Andersen, P, Damkilde, L & Nielsen, S A 2017, ' Statistical methods for damage detection applied to civil structures ', Procedia Engineering, vol. 199, pp. 1919–1924 . https://doi.org/10.1016/j.proeng.2017.09.280
Publication Year :
2017
Publisher :
HAL CCSD, 2017.

Abstract

International audience; 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.

Details

Language :
English
Database :
OpenAIRE
Journal :
EURODYN 2017-10th International Conference on Structural Dynamics, EURODYN 2017-10th International Conference on Structural Dynamics, Sep 2017, Rome, Italy. pp.1919-1924, ⟨10.1016/j.proeng.2017.09.280⟩, Gres, S, Ulriksen, M D, Döhler, M, Johansen, R J, Andersen, P, Damkilde, L & Nielsen, S A 2017, ' Statistical methods for damage detection applied to civil structures ', Procedia Engineering, vol. 199, pp. 1919–1924 . https://doi.org/10.1016/j.proeng.2017.09.280
Accession number :
edsair.doi.dedup.....1790b56046db8893d0cf7bc3bc91d6e8