Early warning for abnormal strains of continuous bridges using a proposed temperature–strain mapping model

Gong, Xiaoyu; Song, Xiaodong; Cai, C S; Li, Guangqi; Xiong, Wen

doi:10.1088/1361-665x/ad0c01

Cited by 3 publications

(2 citation statements)

References 38 publications

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“…This bridge type is a robust structural form that meets considerable force demands. Bridges inevitably experience performance degradation throughout their operation [ 1 , 2 ]. Therefore, the implementation of early warning systems is considered crucial for ensuring the safe operation of bridges [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Early Warning for Continuous Rigid Frame Bridges Based on Nonlinear Modeling for Temperature-Induced Deflection

Jiang,

Yang,

Liu

et al. 2024

Sensors

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Bridge early warning based on structural health monitoring (SHM) system is of significant importance for ensuring bridge safe operation. The temperature-induced deflection (TID) is a sensitive indicator for performance degradation of continuous rigid frame bridges, but the time-lag effect makes it challenging to predict the TID accurately. A bridge early warning method based on nonlinear modeling for the TID is proposed in this article. Firstly, the SHM data of temperature and deflection of a continuous rigid frame bridge are analyzed to examine the temperature gradient variation patterns. Kernel principal component analysis (KPCA) is used to extract principal temperature components. Then, the TID is extracted through wavelet transform, and a nonlinear modeling method for the TID considering the temperature gradient is proposed using the support vector machine (SVM). Finally, the prediction errors of the KPCA-SVM algorithm are analyzed, and the early warning thresholds are determined based on the statistical patterns of the errors. The results show that the KPCA-SVM algorithm achieves high-precision nonlinear modeling for the TID while significantly reducing the computational load. The prediction results have coefficients of determination above 0.98 and fluctuate within a small range with clear statistical patterns. Setting the early warning thresholds based on the statistical patterns of errors enables dynamic and multi-level warnings for bridge structures.

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Section: Introductionmentioning

confidence: 99%

Early Warning for Continuous Rigid Frame Bridges Based on Nonlinear Modeling for Temperature-Induced Deflection

Jiang,

Yang,

Liu

et al. 2024

Sensors

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show abstract

“…Civil infrastructures are susceptible to aging and deterioration caused by severe environmental conditions and excessive loads, posing a significant threat to public safety [1,2]. Structural health monitoring (SHM) has emerged as a crucial solution to mitigate these challenges and plays a critical role in evaluating structural integrity [3][4][5][6]. SHM encompasses the monitoring of variations in structural materials and geometric * Author to whom any correspondence should be addressed.…”

Section: Introductionmentioning

confidence: 99%

Online meta-learning approach for sensor fault diagnosis using limited data

Wang,

Huang,

Huang

et al. 2024

Smart Mater. Struct.

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The accurate and timely diagnosis of sensor faults plays a critical role in ensuring the reliability and performance of structural health monitoring (SHM) systems. However, the challenge is detecting, locating, and estimating sensor faults in an online manner using limited training data. To resolve this problem, a novel approach for online sensor fault diagnosis is proposed for SHM. The proposed approach is based on meta-learning, which enables superior model generalization capabilities using limited data. The detection, localization, and estimation of typical sensor faults in an online manner can be achieved efficiently by the proposed approach. First, a one-dimensional convolutional neural network (1D CNN) is designed to detect and locate faulty sensors. The initial model parameters of the 1D CNN are optimized using a model-agnostic meta-learning training strategy. This strategy allows the acquisition of transferable prior knowledge, which can speed up the learning process on new sensor fault detection and localization tasks. The meta-learning strategy also enables efficient and accurate detection and localization of potential faulty sensors with limited data. After detecting and locating the faulty sensors, an online updating algorithm based on a dual Kalman filter is used to estimate the severity of sensor faults and structural states simultaneously. The proposed approach is demonstrated with simulated sensor faults that cover a numerical example and field measurements from the Canton Tower. The results show that the proposed approach is applicable for online sensor fault diagnosis in SHM.

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Deep learning based anomaly identification of temperature effects in bridge structural health monitoring data

Gong,

Song,

et al. 2024

Structures

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Early warning for abnormal strains of continuous bridges using a proposed temperature–strain mapping model

Cited by 3 publications

References 38 publications

Early Warning for Continuous Rigid Frame Bridges Based on Nonlinear Modeling for Temperature-Induced Deflection

Early Warning for Continuous Rigid Frame Bridges Based on Nonlinear Modeling for Temperature-Induced Deflection

Online meta-learning approach for sensor fault diagnosis using limited data

Deep learning based anomaly identification of temperature effects in bridge structural health monitoring data

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