Thermal response separation for bridge long-term monitoring systems using multi-resolution wavelet-based methodologies

Xu, Xiang; Ren, Yuan; Huang, Qiao; Zhao, Danyang; Tong, Zhaojie; Chang, Weijie

doi:10.1007/s13349-020-00402-7

Cited by 22 publications

(27 citation statements)

References 30 publications

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“…In the data pre-processing, raw signals from SHM systems are first processed using the moving time window method for missing data imputation, which enhances the continuity of recorded signals (Kalaycioglu et al, 2016; Nevalainen et al, 2009). Subsequently, the multi-resolution wavelet-based approach is adopted to address thermal effects based on the distinguished frequency bandwidths (Ni et al, 2012; Xu et al, 2020a). Once obtaining the pre-processed signals, the anomaly detection index is extracted as the energy within a certain time window, where two principal parameters (i.e., length of window and number of overlaps) need to be determined.…”

Section: Methodology For Probabilistic Anomaly Trend Detectionmentioning

confidence: 99%

“…The decomposition level n is determined bywhere f s is the sampling rate of the signal. The specific interpretation regarding the multi-resolution wavelet-based approach is described in our previous paper (Xu et al, 2020a).…”

Section: Methodology For Probabilistic Anomaly Trend Detectionmentioning

confidence: 99%

“…Similar to dynamic characteristics, static parameter-based anomaly detection is affected by environmental factors, especially temperature effects. Plenty of methodologies for thermal effect modeling and separation are developed, such as regression models (Kromanis and Kripakaran, 2014; Ren et al, 2019), wavelet transform (Xu et al, 2020a; Wu et al, 2014), blind source separation (Zhu et al, 2018), numerical model-based methods (Xu et al, 2019; Zhou and Sun, 2019), and Bayesian dynamic linear model (Wang et al, 2019). Considering the influence of thermal effects to static parameter-based anomaly detection, various temperature-driven anomaly detection approaches are developed.…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic anomaly trend detection for cable-supported bridges using confidence interval estimation

Qian

Huang

et al. 2022

Advances in Structural Engineering

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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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Section: Methodology For Probabilistic Anomaly Trend Detectionmentioning

confidence: 99%

Section: Methodology For Probabilistic Anomaly Trend Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Probabilistic anomaly trend detection for cable-supported bridges using confidence interval estimation

Qian

Huang

et al. 2022

Advances in Structural Engineering

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“…To separate signals of aforementioned two ingredients, the multiresolution wavelet method based on distinguished frequency bandwidths is adopted [19].…”

Section: Processing For Monitoring Datamentioning

confidence: 99%

“…To extract different components, blind separation methods are usually adopted [18]. Xu et al [19] used a multiresolution wavelet-based method to separate thermal effects from bridge responses based on the distinguished frequency bandwidths. A practical multivariate linear-based model was also presented to simulate and separate thermal effects from the cable-stayed bridge response [20].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Extreme Cable Forces of Cable‐Stayed Bridges Based on Monitoring Data and Random Vehicle Models

Ren

Zhu

Fan

et al. 2021

Advances in Civil Engineering

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For long-span cable-stayed bridges, cables serve as one of the most important components to guarantee structural integrity. Forces of stay cables indicate not only the performance of cables themselves but also the overall condition of bridges. In order to help stakeholders to make maintenance decisions, an extreme cable force estimation method was proposed based on cable force measurements and traffic data from the weighing system. First, raw monitoring data were preprocessed based on a median filtering to obtain usable cable force signals. The multiresolution wavelet method was used to extract traffic-induced force component from mixed signals. Then, a Monte Carlo-based random vehicle model was developed using traffic data from the weighing system. Based on field temperature measurements and simulation of traffic-induced effects, extreme cable forces with respect to vehicle loads and temperature effects were predicted by extreme value theory. The Generalized Pareto Distribution (GPD) was adopted to establish the probability distribution models of the daily maximum cable force. Then, the extreme value within a return period of 100 years was determined and compared with the design loading demand. Finally, the effectiveness of the proposed method was validated through a cable-stayed bridge in China. As a result, the low-frequency varying component of cable force response had positive correlation with environmental temperatures, and the extreme value of the predicted cable force under prospective traffic volumes was within limit interval value according to the design code. The conclusions can be utilized by bridge owners to make maintenance decisions.

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Correlation model of deflection, vehicle load, and temperature for in‐service bridge using deep learning and structural health monitoring

Deng

Zhai

et al. 2022

Structural Contr & Hlth

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Deflection is an important issue in bridge structural health monitoring. An accurate deflection-vehicle load-temperature correlation model is critical to abnormal data identification, deflection prediction under extreme conditions, and bridge structural assessment. However, because of the discrete distribution in time domain of vehicle load and the extreme complexity of the deflectionvehicle load-temperature correlation, the correlation modeling method needs further studies. A novel deflection-vehicle load-temperature correlation modeling method is developed in this study. Based on the concept of deflection influence line (DIL), the raw vehicle load monitoring data are transformed into time-continuous vehicle influence coefficient (VIC). By using gated recurrent unit (GRU) neural network, a correlation model with inputs of VIC and environmental temperature data and output of deflection data is established.Taking a suspension bridge in China as an example, the prediction accuracy of short-, medium-, and long-term correlation models is tested. Moreover, based on the correlation model, a decomposition method of temperature-and vehicle-induced deflection components is proposed. The results show that the predicted deflection of the short-term correlation model is basically consistent with the real-time monitoring data, while the medium-and long-term correlation models have accurate prediction ability for the deflection extreme values in a certain time window. The temperature-and vehicle-induced deflection components separated by using the correlation model are in good agreement with the wavelet decomposition (WD) results, with clear physical meaning and independent of empirical judgment.

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Thermal response separation for bridge long-term monitoring systems using multi-resolution wavelet-based methodologies

Cited by 22 publications

References 30 publications

Probabilistic anomaly trend detection for cable-supported bridges using confidence interval estimation

Probabilistic anomaly trend detection for cable-supported bridges using confidence interval estimation

Estimation of Extreme Cable Forces of Cable‐Stayed Bridges Based on Monitoring Data and Random Vehicle Models

Correlation model of deflection, vehicle load, and temperature for in‐service bridge using deep learning and structural health monitoring

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