Real-Time Dynamic Warning on Deflection Abnormity of Cable-Stayed Bridges Considering Operational Environment Variations

Fan, Ziyuan; Huang, Qiao; Ren, Yuan; Xu, Xiang; Zhu, Zhihui

doi:10.1061/(asce)cf.1943-5509.0001537

Cited by 15 publications

(11 citation statements)

References 33 publications

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“…The dynamic thresholds of deflections for given relative location indices can be derived by the Bayesian inference using equations ( 10) and (11). Once this probabilistic monitoring model is constructed, the detection of the unusual deflections can be conducted.…”

Section: Anomaly Scoring Methodsmentioning

confidence: 99%

“…As such, dynamic thresholds have been introduced in several previous studies. 2,10,11 Ding et al (2017) suggested a method to set a dynamic threshold for the acceleration amplitude of a bridge girder at various train speeds. 1 Zhao et al (2019) studied the time-dependent in-service behaviors of a long-span railway bridge, and the early warnings of hourly deflections of the bridge subject to the coupled effects of temperatures and train loads.…”

Section: Introductionmentioning

confidence: 99%

“…2 Fan et al (2021) developed dynamic thresholds which varied with operational conditions, so as to provide warnings regarding deflection abnormalities in bridges based on the monitoring data. 11 In addition, many factors affecting bridge responses, such as the material and geometrical properties of a bridge structure, train running speed, self-weight of a train (including passenger volume inside), and environmental conditions may have aleatoric and epistemic uncertainties. To efficiently consider those uncertain factors, the Bayesian inference method was introduced for the purpose of structural monitoring.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sensor data-based probabilistic monitoring of time-history deflections of railway bridges induced by high-speed trains

Lee

Jeong

Sim

et al. 2022

Structural Health Monitoring

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Structural condition monitoring of railway bridges has been emphasized for guaranteeing the passenger comfort and safety. Various attempts have been made to monitor structural conditions, but many of them have focused on monitoring dynamic characteristics in frequency domain representation which requires additional data transformation. Occurrence of abnormal structural responses, however, can be intuitively detected by directly monitoring the time-history responses, and it may give information including the time to occur the abnormal responses and the magnitude of the dynamic amplification. Therefore, this study suggests a new Bayesian method for directly monitoring the time-history deflections induced by high-speed trains. To train the monitoring model, the data preprocessing of speed estimation and data synchronization are conducted first for the given training data of the raw time-history deflection; the Bayesian inference is then introduced for the derivation of the probability-based dynamic thresholds for each train type. After constructing the model, the detection of the abnormal deflection data is proceeded. The speed estimation and data synchronization are conducted again for the test data, and the anomaly score and ratio are estimated based on the probabilistic monitoring model. A warning is generated if the anomaly ratio is at an unacceptable level; otherwise, the deflection is considered as a normal condition. A high-speed railway bridge in operation is chosen for the verification of the proposed method, in which a probabilistic monitoring model is constructed from displacement time-histories during train passage. It is shown that the model can specify an anomaly of a train-track-bridge system.

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Section: Anomaly Scoring Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sensor data-based probabilistic monitoring of time-history deflections of railway bridges induced by high-speed trains

Lee

Jeong

Sim

et al. 2022

Structural Health Monitoring

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show abstract

“…The predicted deflection can provide supports for decision making of bridge maintenance. Third, the main purpose of deflection monitoring is to infer stiffness changes and diagnose the potential structural damage 13,14 . The aforementioned correlation model can be used to determine whether the real‐time deflection monitoring data has deviated from the normal changing range.…”

Section: Introductionmentioning

confidence: 99%

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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Continuous monitoring of in-service performance of prestressed concrete continuous bridges with two strengthening measures

Song

Gong

et al. 2022

Construction and Building Materials

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Real-Time Dynamic Warning on Deflection Abnormity of Cable-Stayed Bridges Considering Operational Environment Variations

Cited by 15 publications

References 33 publications

Sensor data-based probabilistic monitoring of time-history deflections of railway bridges induced by high-speed trains

Sensor data-based probabilistic monitoring of time-history deflections of railway bridges induced by high-speed trains

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

Continuous monitoring of in-service performance of prestressed concrete continuous bridges with two strengthening measures

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