Damage Detection in Active Suspension Bridges: An Experimental Investigation

Meng, Fanhao; Mokrani, Bilal; Alaluf, David; Yu, Juebang; Preumont, André

doi:10.3390/s18093002

Cited by 17 publications

(17 citation statements)

References 45 publications

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“…Thus, for both cases (numerical and real scenarios), the WE allowed for damage identification; however, in order to use just a few sensors in practical cases, the WEAM must be applied by using a controlled test. Some of the most promising and recent researches with the same aim of detecting damage (especially damaged cables) in cable-supported bridges can be found in [38][39][40][41][42][43][44][45]. Most of those studies have focused on proposing methods for identifying damage by using only numerical simulations and academic experiments, obtaining an accuracy higher than 60% [38][39][40][41][42][43].…”

Section: Results Discussionmentioning

confidence: 99%

Wavelet Energy Accumulation Method Applied on the Rio Papaloapan Bridge for Damage Identification

et al. 2021

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Large civil structures such as bridges must be permanently monitored to ensure integrity and avoid collapses due to damage resulting in devastating human fatalities and economic losses. In this article, a wavelet-based method called the Wavelet Energy Accumulation Method (WEAM) is developed in order to detect, locate and quantify damage in vehicular bridges. The WEAM consists of measuring the vibration signals on different points along the bridge while a vehicle crosses it, then those signals and the corresponding ones of the healthy bridge are subtracted and the Continuous Wavelet Transform (CWT) is applied on both, the healthy and the subtracted signals, to obtain the corresponding diagrams, which provide a clue about where the damage is located; then, the border effects must be eliminated. Finally, the Wavelet Energy (WE) is obtained by calculating the area under the curve along the selected range of scale for each point of the bridge deck. The energy of a healthy bridge is low and flat, whereas for a damaged bridge there is a WE accumulation at the damage location. The Rio Papaloapan Bridge (RPB) is considered for this research and the results obtained numerically and experimentally are very promissory to apply this method and avoid accidents.

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Section: Results Discussionmentioning

confidence: 99%

Wavelet Energy Accumulation Method Applied on the Rio Papaloapan Bridge for Damage Identification

et al. 2021

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“…However, the time-domain data from the sensor usually contains noise, which requires signal processing techniques to minimize the effect of the noise. In literature, different signal processing techniques have been developed to denoise and/or extract damage features from the time-domain vibration data, such as random decrement method [120], wavelet transform [121], empirical modes decomposition [122] and Hilbert-Huang transform [123].…”

Section: Vibration Measurementmentioning

confidence: 99%

Sensor Networks for Structures Health Monitoring: Placement, Implementations, and Challenges—A Review

Mustapha

et al. 2021

Vibration

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The development of structural health monitoring (SHM) systems and their integration in actual structures has become a necessity as it can provide a robust and low-cost solution for monitoring the structural integrity of and the ability to predict the remaining life of structures. In this review, we aim at focusing on one of the important issues of SHM, the design, and implementation of sensor networks. Location and number of sensors, in any SHM system, are of high importance as they impact the system integration, system performance, and accuracy of assessment, as well as the total cost. Hence we are interested in shedding the light on the sensor networks as an essential component of SHM systems. The review discusses several important parameters including design and optimization of sensor networks, development of academic and commercial solutions, powering of sensors, data communication, data transmission, and analytics. Finally, we presented some successful case studies including the challenges and limitations associated with the sensor networks.

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“…Due to the high uncertainty of the measurement and finite element modeling, Ding et al [10] established the objective function using modal data and sparse regularization technology, and they developed a hybrid group intelligence technology involving the Jaya and tree-seed algorithm to identify structural damage. Meng et al [11] used modal flexibility as DSF and performed damage diagnosis of suspension bridge hangers based on a genetic algorithm. Guan et al [12] recruited wavelet coefficient modulus maxima as DSF and employed an artificial neural network (ANN) and particle swarm algorithm to complete the damage identification of a suspension bridge.…”

Section: Introductionmentioning

confidence: 99%

Damage Identification of Long-Span Bridges Using the Hybrid of Convolutional Neural Network and Long Short-Term Memory Network

Tang

Gao³

et al. 2021

Algorithms

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The shallow features extracted by the traditional artificial intelligence algorithm-based damage identification methods pose low sensitivity and ignore the timing characteristics of vibration signals. Thus, this study uses the high-dimensional feature extraction advantages of convolutional neural networks (CNNs) and the time series modeling capability of long short-term memory networks (LSTM) to identify damage to long-span bridges. Firstly, the features extracted by CNN and LSTM are fused as the input of the fully connected layer to train the CNN-LSTM model. After that, the trained CNN-LSTM model is employed for damage identification. Finally, a numerical example of a large-span suspension bridge was carried out to investigate the effectiveness of the proposed method. Furthermore, the performance of CNN-LSTM and CNN under different noise levels was compared to test the feasibility of application in practical engineering. The results demonstrate the following: (1) the combination of CNN and LSTM is satisfactory with 94% of the damage localization accuracy and only 8.0% of the average relative identification error (ARIE) of damage severity identification; (2) in comparison to the CNN, the CNN-LSTM results in superior identification accuracy; the damage localization accuracy is improved by 8.13%, while the decrement of ARIE of damage severity identification is 5.20%; and (3) the proposed method is capable of resisting the influence of environmental noise and acquires an acceptable recognition effect for multi-location damage; in a database with a lower signal-to-noise ratio of 3.33, the damage localization accuracy of the CNN-LSTM model is 67.06%, and the ARIE of the damage severity identification is 31%. This work provides an innovative idea for damage identification of long-span bridges and is conducive to promote follow-up studies regarding structural condition evaluation.

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Damage Detection in Active Suspension Bridges: An Experimental Investigation

Cited by 17 publications

References 45 publications

Wavelet Energy Accumulation Method Applied on the Rio Papaloapan Bridge for Damage Identification

Wavelet Energy Accumulation Method Applied on the Rio Papaloapan Bridge for Damage Identification

Sensor Networks for Structures Health Monitoring: Placement, Implementations, and Challenges—A Review

Damage Identification of Long-Span Bridges Using the Hybrid of Convolutional Neural Network and Long Short-Term Memory Network

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