Probabilistic anomaly trend detection for cable-supported bridges using confidence interval estimation
To rate uncertainties within anomaly detection course for large span cable-supported bridges, a probabilistic approach is developed based on confidence interval estimation of extreme value analytics. First, raw signals from structural health monitoring system are pre-processed, including missing data imputation using moving time window mean imputation approach and thermal response separation through multi-resolution wavelet-based method. Then, an energy index is extracted from time domain signals to enhance robust of detection performance. A resampling-based method, namely the bootstrap, is adopted herein for confidence interval estimation. Four confidence levels are defined for the anomaly trend detection in this study, namely 95%, 80%, 50%, and 20%. Finally, the effectiveness of the proposed anomaly trend detection methodology is validated by using in-situ cable force measurements from the Nanjing Dashengguan Yangtze River Bridge. As a result, the four-level anomaly detection triggers are determined by using the confidence interval estimation based on cable force measurements in 2007, which are 58,671, 48,862, 42,499 and 39,035, respectively. Subsequently, three cases are presented, which are spike detection, overloading vehicle detection and snow disaster detection. Through the spike detection, it is verified that energy index is capable to tolerate signal spikes. Three overloading events are simulated to conduct overloading vehicle detections. As a result, the three overloading events are detected successfully associated with different confidences. Snow disaster is detected with a more than 80% confidence based on the field measurements during the snow storm time window.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/13694332
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Supplemental Notes:
- © The Author(s) 2022.
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Authors:
- Xu, Xiang
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0000-0002-9412-667X
- Qian, Zhen-Dong
- Huang, Qiao
- Ren, Yuan
- Liu, Bin
- Publication Date: 2022-4
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 966-978
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Serial:
- Advances in Structural Engineering
- Volume: 25
- Issue Number: 5
- Publisher: Sage Publications, Incorporated
- ISSN: 1369-4332
- Serial URL: http://journals.sagepub.com/home/ase
Subject/Index Terms
- TRT Terms: Bridge cables; Confidence intervals; Flaw detection; Long span bridges; Structural health monitoring
- Identifier Terms: Nanjing Dashengguan Yangtze River Bridge
- Subject Areas: Bridges and other structures; Highways; Maintenance and Preservation;
Filing Info
- Accession Number: 01834678
- Record Type: Publication
- Files: TRIS
- Created Date: Jan 27 2022 5:32PM