Probabilistic anomaly trend detection for cable-supported bridges using confidence interval estimation
نویسندگان
چکیده
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 and thermal response separation through multi-resolution wavelet-based method. Then, an energy index extracted domain to enhance robust performance. A resampling-based method, namely the bootstrap, adopted herein estimation. Four levels defined trend in this study, 95%, 80%, 50%, 20%. Finally, effectiveness proposed methodology validated by in-situ cable force measurements Nanjing Dashengguan Yangtze River Bridge. As result, four-level triggers determined 2007, which 58,671, 48,862, 42,499 39,035, respectively. Subsequently, three cases presented, spike detection, overloading vehicle snow disaster detection. Through it verified that capable tolerate signal spikes. Three events simulated conduct detections. detected successfully associated with different confidences. Snow more than 80% field during storm window.
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ژورنال
عنوان ژورنال: Advances in Structural Engineering
سال: 2022
ISSN: ['1369-4332', '2048-4011']
DOI: https://doi.org/10.1177/13694332211056108