Reference Surface-Based System Identification

  • Environmental and operational variables and their impact on structural responses have been acknowledged as one of the most important challenges for the application of the ambient vibration-based damage identification in structures. The damage detection procedures may yield poor results, if the impacts of loading and environmental conditions of the structures are not considered. TheEnvironmental and operational variables and their impact on structural responses have been acknowledged as one of the most important challenges for the application of the ambient vibration-based damage identification in structures. The damage detection procedures may yield poor results, if the impacts of loading and environmental conditions of the structures are not considered. The reference-surface-based method, which is proposed in this thesis, is addressed to overcome this problem. In the proposed method, meta-models are used to take into account significant effects of the environmental and operational variables. The usage of the approximation models, allows the proposed method to simply handle multiple non-damaged variable effects simultaneously, which for other methods seems to be very complex. The input of the meta-model are the multiple non-damaged variables while the output is a damage indicator. The reference-surface-based method diminishes the effect of the non-damaged variables to the vibration based damage detection results. Hence, the structure condition that is assessed by using ambient vibration data at any time would be more reliable. Immediate reliable information regarding the structure condition is required to quickly respond to the event, by means to take necessary actions concerning the future use or further investigation of the structures, for instance shortly after extreme events such as earthquakes. The critical part of the proposed damage detection method is the learning phase, where the meta-models are trained by using input-output relation of observation data. Significant problems that may encounter during the learning phase are outlined and some remedies to overcome the problems are suggested. The proposed damage identification method is applied to numerical and experimental models. In addition to the natural frequencies, wavelet energy and stochastic subspace damage indicators are used.show moreshow less

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Metadaten
Document Type:Doctoral Thesis
Author:Dr.-Ing. Sofyan Ahmad
DOI (Cite-Link):https://doi.org/10.25643/bauhaus-universitaet.2113Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20140205-21132Cite-Link
Series (Serial Number):ISM-Bericht // Institut für Strukturmechanik, Bauhaus-Universität Weimar (2013,3)
Referee:Prof. Dr.-Ing. habil. Carsten KönkeORCiDGND, Prof. Dr. techn. Christian Bucher, Prof. Dr.-Ing. Frank Wuttke
Advisor:Prof. Dr.-Ing. habil. Carsten KönkeORCiDGND
Language:English
Date of Publication (online):2014/02/04
Year of first Publication:2013
Date of final exam:2013/09/05
Release Date:2014/02/05
Publishing Institution:Bauhaus-Universität Weimar
Granting Institution:Bauhaus-Universität Weimar, Fakultät Bauingenieurwesen
Institutes:Fakultät Bauingenieurwesen / Institut für Strukturmechanik
Pagenumber:153
Tag:Referenzfläche; Schadensdetektionsverfahren
GND Keyword:System Identification
Dewey Decimal Classification:600 Technik, Medizin, angewandte Wissenschaften / 660 Chemische Verfahrenstechnik / 660 Chemische Verfahrenstechnik
BKL-Classification:58 Chemische Technik, Umwelttechnik, verschiedene Techno-
Licence (German):License Logo Creative Commons 4.0 - Namensnennung (CC BY 4.0)