Vehicle axle identification using wavelet analysis of bridge global responses

Yu, Yang; Cai, C.S.; Deng, Lu

doi:10.1177/1077546315623147

Cited by 46 publications

(34 citation statements)

References 8 publications

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“…The results show that the wavelet techniques can help identify closely spaced axles within a tandem or tridem group which could not be directly identified from the FAD signal and reveal the potential of using the wavelet techniques to identify vehicle axles from the strain signal of weighing sensors. Yu et al (2015) proposed a vehicle axle identification method based on the wavelet transformation of the global signal. The numerical results showed that this method could provide accurate identification of vehicle axles using only the weighing sensors.…”

Section: Axle Detectionmentioning

confidence: 99%

State-of-the-art review on bridge weigh-in-motion technology

Cai

Deng

2016

Advances in Structural Engineering

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190

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Weigh-in-motion technology is an effective tool that has been extensively used to monitor traffic on highways. Pavement-based weighin-motion systems usually have poor durability and will cause traffic interruption during their installation and maintenance process. The recently developed bridge weigh-in-motion technology provides a more convenient and cost-effective alternative to the pavement-based weigh-in-motion technology. Bridge weigh-in-motion systems can be installed without interrupting the traffic. Also, bridge weigh-in-motion systems have the potential to deliver better accuracy than pavement-based weigh-in-motion systems. Due to these significant advantages, the bridge weigh-in-motion technology has been playing an increasingly important role in bridge health monitoring and overweight truck enforcement, and many studies have been conducted to continuously improve the bridge weigh-inmotion technology. In this review, the common algorithms for bridge weigh-in-motion are discussed in detail, and the typical instrumentation of bridge weigh-in-motion systems is also introduced. Meanwhile, much effort is made to identify the remaining issues in the application of bridge weigh-in-motion technology, and the corresponding future research is proposed.

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Section: Axle Detectionmentioning

confidence: 99%

State-of-the-art review on bridge weigh-in-motion technology

Cai

Deng

2016

Advances in Structural Engineering

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190

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“…Furthermore, based on the electrical resistance, the strain sensor senses the vehicles by its deformation. When installed under a bridge deck, the signals of a strain sensor peaks when wheels pass over the observation section [ 14 , 15 ]. Wavelet analysis [ 16 ] is proposed to detect a moving vehicle, and furthermore, to recognize the axles as well as classify the vehicle type.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Based Vehicle Detection and Classification Methodology Using Strain Sensors under Bridge Deck

Zhang

Dong

et al. 2020

Sensors

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Vehicle detection and classification have become important tasks for traffic monitoring, transportation management and pavement evaluation. Nowadays there are sensors to detect and classify the vehicles on road. However, on one hand, most sensors rely on direct contact measurement to detect the vehicles, which have to interrupt the traffic. On the other hand, complex road scenes produce much noise to consider when to process the signals. In this paper, a data-driven methodology for the detection and classification of vehicles using strain data is proposed. The sensors are well arranged under the bridge deck without traffic interruption. Next, a cascade pre-processing method is applied for vehicle detection to eliminate in-situ noise. Then, a neural network model is trained to identify the close-range following vehicles and separate them by Non-Maximum Suppression. Finally, a deep convolutional neural network is designed and trained to identify the vehicle types based on the axle group. The methodology was applied in a long-span bridge. Three strain sensors were installed beneath the bridge deck for a week. High robustness and accuracy were obtained by these algorithms. The methodology proposed in this paper is an adaptive and promising method for vehicle detection and classification under complex noise. It would serve as a supplement to current transportation systems and provide reliable data for management and decision-making.

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“…To better describe the accuracy of identification, the identification error (IE) is defined as (Yu et al, 2017) Identification error = P iden À P true P true 3 100% ð6Þ…”

Section: Numerical Examplementioning

confidence: 99%

Vehicle weight identification for a bridge with multi-T-girders based on load transverse distribution coefficient

Zuo

Ren

2019

Advances in Structural Engineering

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It is still an issue to identify the vehicle weight for bridges with multi-T-girders which is essential in bridge operation management. In this study, a vehicle weight identification method is proposed for bridges with multi-T-girders. To consider the load transverse distribution effect, first the separation variable method is adopted to separate the spatial influence surface function of vehicle load into the product of two single-valued functions. Then the procedures of the vehicle weight identification method are proposed using the measured strain responses of the T-girders caused by the passing vehicle accordingly. A numerical example of a bridge with five T-girders and an experimental example of a bridge with two T-girders are conducted to verify the feasibility and effectiveness of the proposed method. Furthermore, the performance of the proposed method is compared with the typical existing method in which the load transverse distribution effect is not considered.

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Vehicle axle identification using wavelet analysis of bridge global responses

Cited by 46 publications

References 8 publications

State-of-the-art review on bridge weigh-in-motion technology

State-of-the-art review on bridge weigh-in-motion technology

Deep Learning Based Vehicle Detection and Classification Methodology Using Strain Sensors under Bridge Deck

Vehicle weight identification for a bridge with multi-T-girders based on load transverse distribution coefficient

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