A signal recovery method for bridge monitoring system using TVFEMD and encoder-decoder aided LSTM

Xin, Jingzhou; Zhou, Chaoying; Jiang, Yan; Tang, Qizhi; Yang, Xianyi; Zhou, Jianting

doi:10.1016/j.measurement.2023.112797

Cited by 39 publications

(25 citation statements)

References 39 publications

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“…l R is the effective magnetic circuit length of the induction coil. According to electromagnetic induction law, the induced voltage of the induction coil can be obtained by the magnetic flux in the area around the coil [37], as shown in Equation (7), where Φ is the magnetic flux around the area of the induction coil, and t is the time.…”

Section: Theorymentioning

confidence: 99%

“…Furthermore, the loss of vertical prestress has an important influence on the principal tensile stress of the box girder web [4]. Once the vertical prestress is lost and the web cracks, the bridge structure's safety and durability will be affected [5][6][7]. Therefore, the vertical prestressed rebar working stress must be accurately monitored to ensure the structure's safety.…”

Section: Introductionmentioning

confidence: 99%

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Working Stress Measurement of Prestressed Rebars Using the Magnetic Resonance Method

et al. 2023

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Prestressed rebars are usually used to apply vertical prestress to concrete to prevent web cracking. The reduction of working stress will affect the durability of the structure. However, the existing working stress detection methods for prestressed rebars still need to be improved. To monitor the working stress of rebars, a magnetic resonance sensor was introduced to carry out experimental research. The correlation between rebar stress and the sensor’s induced voltage was theoretically analyzed using the magnetoelastic effect and magnetic resonance theory. A working stress monitoring method for prestressed rebars based on magnetic resonance was proposed. Working stress monitoring experiments were carried out for 16 mm, 18 mm, and 20 mm diameter rebars. The results showed that the induced voltage peak-to-peak value and the rebar prestress were nonlinearly correlated under different working conditions. Correlations between the characteristic indicators and the rebar working stress were obtained using nonlinear and linear fit. The cubic polynomial segmented fit outperformed the gradient overall linear fit, with the goodness of fit R2 greater than 0.96. The average relative error values of working stress monitoring were less than 5% under different working conditions. This provides a new method for working stress measurement of vertical prestressed rebars.

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Section: Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Working Stress Measurement of Prestressed Rebars Using the Magnetic Resonance Method

et al. 2023

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“…In recent years, to address the limitations of existing methods, some scholars have proposed time varying filtering based empirical mode decomposition (TVFEMD) [28]. This method can effectively decompose non-stationary sequences into a combination of stationary sequences, improving data quality and prediction effectiveness [29,30]. However, the application of TVFEMD to complex signal decomposition and missing data recovery is currently limited.…”

Section: Introductionmentioning

confidence: 99%

A data recovery method for extra-long-span railway bridge health monitoring based on TVFEMD and CNN-GRU

Hao,

Liu,

et al. 2024

Meas. Sci. Technol.

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Structure health monitoring (SHM) systems play a crucial role in understanding the condition of structures. However, owing to various uncertain factors, sensor data may be missing, which poses a great challenge to capture the real-time dynamic characteristics of the bridge. To address this problem, this paper proposes a model that integrates time varying filtering based empirical mode decomposition (TVFEMD), convolutional neural network (CNN), and gated recurrent unit (GRU) to forecast and fill in the missing data. The proposed model initially employs TVFEMD to decompose the signal into several intrinsic mode functions (IMFs) of different frequency bands. Subsequently, CNN is utilized to extract data features for each IMF, followed by prediction through GRU. The model linearly combines the prediction results obtained from each IMF to obtain the actual prediction result. Simulation data and measured data from the large railway bridge are utilized in this research to validate the model’s efficacy. The analysis results demonstrate a significant improvement in prediction performance compared to traditional models, showcasing strong generalization ability and robustness. In conclusion, the model proposed in this paper has a high utilization value in health monitoring data recovery.

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“…The local damage monitoring method uses advanced sensors, non-destructive testing and other means to directly diagnose the local damage status of the structure. By contrast, the overall damage identification method uses data mining of structural response information to indirectly extract the characterization index of the structural damage status [6][7][8][9][10][11][12]. As a large and complex civil structure, arch bridges have many key components, and the damage monitoring and identification methods of arch bridges are complicated.…”

Section: Introductionmentioning

confidence: 99%

A Review on Damage Monitoring and Identification Methods for Arch Bridges

et al. 2023

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The damage monitoring and identification of arch bridges provide an important means to ensure the safe operation of arch bridges. At present, many methods have been developed, and the applicability and effectiveness of these methods depend on the damage type, structural configuration and available data. To guide the practical application of these methods, a systematic review is implemented in this paper. Specifically, the damage monitoring and identification methods of arch bridges are divided into the damage monitoring of local diseases and damage identification of overall performance. Firstly, the research on the damage monitoring of the local diseases of arch bridges is reviewed. According to the disease type, it is divided into four categories, including suspender inspection, void monitoring, stress detection and corrosion detection. For each disease, this paper analyzes the principles, advantages and shortcomings of various methods. Then, the damage identification methods of the overall performance of arch bridges are reviewed, including masonry arch bridges, steel arch bridges, reinforced concrete arch bridges and concrete-filled steel tubular arch bridges. And the commonly used damage indexes of damage identification methods are summarized. This review aims to help researchers and practitioners in implementing existing damage detection methods effectively and developing more reliable and practical methods for arch bridges in the future.

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A signal recovery method for bridge monitoring system using TVFEMD and encoder-decoder aided LSTM

Cited by 39 publications

References 39 publications

Working Stress Measurement of Prestressed Rebars Using the Magnetic Resonance Method

Working Stress Measurement of Prestressed Rebars Using the Magnetic Resonance Method

A data recovery method for extra-long-span railway bridge health monitoring based on TVFEMD and CNN-GRU

A Review on Damage Monitoring and Identification Methods for Arch Bridges

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