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RISS 인기검색어
- 검색키워드
- 저자 : Gardoni, Paolo (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
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Iterative damage index method for structural health monitoring
You, Taesun,Gardoni, Paolo,Hurlebaus, Stefan Techno-Press 2014 Structural monitoring and maintenance Vol.1 No.1
Structural Health Monitoring (SHM) is an effective alternative to conventional inspections which are time-consuming and subjective. SHM can detect damage early and reduce maintenance cost and thereby help reduce the likelihood of catastrophic structural events to infrastructure such as bridges. After reviewing the Damage Index Method (DIM), an Iterative Damage Index Method (IDIM) is proposed to improve the accuracy of damage detection. These two damage detection techniques are compared based on damage on two structures, a simply supported beam and a pedestrian bridge. Compared to the traditional damage detection algorithm, the proposed IDIM is shown to be less arbitrary and more accurate.
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Assessment of modal parameters considering measurement and modeling errors
Huang, Qindan,Gardoni, Paolo,Hurlebaus, Stefan Techno-Press 2015 Smart Structures and Systems, An International Jou Vol.15 No.3
Modal parameters of a structure are commonly used quantities for system identification and damage detection. With a limited number of studies on the statistics assessment of modal parameters, this paper presents procedures to properly account for the uncertainties present in the process of extracting modal parameters. Particularly, this paper focuses on how to deal with the measurement error in an ambient vibration test and the modeling error resulting from a modal parameter extraction process. A bootstrap approach is adopted, when an ensemble of a limited number of noised time-history response recordings is available. To estimate the modeling error associated with the extraction process, a model prediction expansion approach is adopted where the modeling error is considered as an "adjustment" to the prediction obtained from the extraction process. The proposed procedures can be further incorporated into the probabilistic analysis of applications where the modal parameters are used. This study considers the effects of the measurement and modeling errors and can provide guidance in allocating resources to improve the estimation accuracy of the modal data. As an illustration, the proposed procedures are applied to extract the modal data of a damaged beam, and the extracted modal data are used to detect potential damage locations using a damage detection method. It is shown that the variability in the modal parameters can be considered to be quite low due to the measurement and modeling errors; however, this low variability has a significant impact on the damage detection results for the studied beam.
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Assessment of modal parameters considering measurement and modeling errors
Qindan Huang,Paolo Gardoni,Stefan Hurlebaus 국제구조공학회 2015 Smart Structures and Systems, An International Jou Vol.15 No.3
Modal parameters of a structure are commonly used quantities for system identification anddamage detection. With a limited number of studies on the statistics assessment of modal parameters, thispaper presents procedures to properly account for the uncertainties present in the process of extracting modalparameters. Particularly, this paper focuses on how to deal with the measurement error in an ambientvibration test and the modeling error resulting from a modal parameter extraction process. A bootstrapapproach is adopted, when an ensemble of a limited number of noised time-history response recordings isavailable. To estimate the modeling error associated with the extraction process, a model predictionexpansion approach is adopted where the modeling error is considered as an “adjustment” to the predictionobtained from the extraction process. The proposed procedures can be further incorporated into theprobabilistic analysis of applications where the modal parameters are used. This study considers the effectsof the measurement and modeling errors and can provide guidance in allocating resources to improve theestimation accuracy of the modal data. As an illustration, the proposed procedures are applied to extract themodal data of a damaged beam, and the extracted modal data are used to detect potential damage locationsusing a damage detection method. It is shown that the variability in the modal parameters can be consideredto be quite low due to the measurement and modeling errors; however, this low variability has a significantimpact on the damage detection results for the studied beam.
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