Traffic Sensing Methodology Combining Influence Line Theory and Computer Vision Techniques for Girder Bridges

Jian, Xudong; Xia, Ye; Galant, José Antonio Lozano; Sun, Lijun

doi:10.1155/2019/3409525

Cited by 50 publications

(35 citation statements)

References 26 publications

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“…As for simple traffic scenarios such as only one vehicle passing the bridge, the recognition of vehicle type, velocity and axle numbers has already been performed with certain accuracy [30]. This paper would focus on a more challenging problem: elaborating the GVW recognition method on the multiple-vehicle problem that remains to be solved.…”

Section: Identification Results For Complex Scenariosmentioning

confidence: 99%

“…However, these data are generally unavailable because of some field conditions. For this reason, a new method is proposed in the companion paper [30], which obtains essential parameters directly from the video image with simply two lines of equal space length in the image regardless of the camera location and/or its orientation. For the conciseness of this paper, that method is not elaborated herein.…”

Section: Coordinate Transformationmentioning

confidence: 99%

“…The whole procedure is shown in Figure 3 for illustration. Details of implementation are available in the literature [30].…”

Section: Bridge Strain Sensingmentioning

confidence: 99%

“…The whole procedure is shown in Figure 3 for illustration. Details of implementation are available in the literature [30]. It is noteworthy that the above discussion is merely applicable for short and medium span bridges that are commonly chosen as the targets of BWIM implementation [32].…”

Section: Bridge Strain Sensingmentioning

confidence: 99%

“…This paper employs a method fitting strain influence line with measured strain data from field calibration tests. The method includes two steps [30]: first, the shape of the strain influence line of the target bridge is theoretically obtained with the kinematic method according to Timoshenko and Young [37]; second, a truck with known weight is arranged to cross the instrumented bridge several times as the calibration tests. The strain data measured in the tests is used to determine the exact It is noteworthy that the above discussion is merely applicable for short and medium span bridges that are commonly chosen as the targets of BWIM implementation [32].…”

Section: Traffic Load and Bridge Reactionmentioning

confidence: 99%

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Infrastructure Safety Oriented Traffic Load Monitoring Using Multi-Sensor and Single Camera for Short and Medium Span Bridges

et al. 2019

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A reliable and accurate monitoring of traffic load is of significance for the operational management and safety assessment of bridges. Traditional weight-in-motion techniques are capable of identifying moving vehicles with satisfactory accuracy and stability, whereas the cost and construction induced issues are inevitable. A recently proposed traffic sensing methodology, combining computer vision techniques and traditional strain based instrumentation, achieves obvious overall improvement for simple traffic scenarios with less passing vehicles, but are enfaced with obstacles in complicated traffic scenarios. Therefore, a traffic monitoring methodology is proposed in this paper with extra focus on complicated traffic scenarios. Rather than a single sensor, a network of strain sensors of a pre-installed bridge structural health monitoring system is used to collect redundant information and hence improve accuracy of identification results. Field tests were performed on a concrete box-girder bridge to investigate the reliability and accuracy of the method in practice. Key parameters such as vehicle weight, velocity, quantity, type and trajectory are effectively identified according to the test results, in spite of the presence of one-by-one and side-by-side vehicles. The proposed methodology is infrastructure safety oriented and preferable for traffic load monitoring of short and medium span bridges with respect to accuracy and cost-effectiveness.

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Section: Identification Results For Complex Scenariosmentioning

confidence: 99%

Section: Coordinate Transformationmentioning

confidence: 99%

“…The whole procedure is shown in Figure 3 for illustration. Details of implementation are available in the literature [30].…”

Section: Bridge Strain Sensingmentioning

confidence: 99%

Section: Bridge Strain Sensingmentioning

confidence: 99%

Section: Traffic Load and Bridge Reactionmentioning

confidence: 99%

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Infrastructure Safety Oriented Traffic Load Monitoring Using Multi-Sensor and Single Camera for Short and Medium Span Bridges

et al. 2019

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An accurate and robust monitoring method of full‐bridge traffic load distribution based on YOLO‐v3 machine vision

Dan

2020

Struct Control Health Monit

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The accurate and stable identification of the traffic load distribution on the bridge deck is of great significance to bridge health monitoring and safety early warning. To accomplish this task, we have combined the weigh-in-motion system (WIMs) with machine vision and developed a traffic load monitoring (TLM) technology for the whole bridge deck. For bridge health monitoring, the TLM should be available for online structural analysis, have high accuracy, and be able to adapt to changes in lighting conditions. However, existing TLM methods are difficult to meet the requirements of real-time, accuracy, and lighting robustness simultaneously. In this regard, this paper proposes an improved full-bridge TLM method based on YOLO-v3 convolutional neural network. The core of this method includes training a dual-target detection model and correcting vehicle locations. The detection model can identify profiles of the entire vehicle and its tail and can mark them with compact rectangular boxes. Based on the corner points of these rectangular boxes, an optical geometry model is proposed to measure vehicle dimensions and correct vehicle centroids, thereby the vehicle locations can be estimated more accurately. By applying the time synchronization of cameras and the WIMs, each measured load is paired with the vehicle "pixel cluster" detected in the video; further, the traffic load distribution on the whole bridge deck is identified accurately in real-time. Verified by the field data of a ramp bridge, the proposed method is proved more accurately on the identification of vehicle locations, more robust lighting adaptability, and faster calculation speed, which can meet the requirements of field monitoring of traffic load distribution.

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A robust bridge weigh‐in‐motion algorithm based on regularized total least squares with axle constraints

Jian

Lai

Xia

et al. 2022

Structural Contr & Hlth

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Summary The identification of traffic loads, including the axle weight (AW) and the gross vehicle weight (GVW) of vehicles, plays an important role in bridge design refinement, safety evaluation, and maintenance strategies. Bridge weigh in motion (BWIM) is a promising technique to weigh vehicles passing through bridges. Though the state‐of‐the‐art BWIM can accurately identify the GVW, unacceptable weighing errors are reported when identifying the AW of vehicles, particularly for those with closely spaced axles. To address the axle weighing problem, this paper aims to improve the performance of BWIM in weighing individual axle loads apart from the gross vehicle weight. The work first theoretically analyzes the possible sources of errors of existing BWIM algorithms, which are observational errors residing in the BWIM equation, no constraint imposed on individual axle loads, and ill‐conditioned nature. Accordingly, three measures are taken to establish a novel robust BWIM algorithm, which is based on regularized total least squares, as well as imposing constraints on the relationship between axles. To validate the proposed algorithm, a series of weighing experiments are carried out on a high‐fidelity vehicle‐bridge scale model. The corresponding results indicate that the proposed BWIM algorithm significantly outperforms other existing BWIM algorithms in terms of the accuracy and robustness of identifying individual axle weight, while retaining satisfactory identification of the gross vehicle weight as well.

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Traffic Sensing Methodology Combining Influence Line Theory and Computer Vision Techniques for Girder Bridges

Cited by 50 publications

References 26 publications

Infrastructure Safety Oriented Traffic Load Monitoring Using Multi-Sensor and Single Camera for Short and Medium Span Bridges

Infrastructure Safety Oriented Traffic Load Monitoring Using Multi-Sensor and Single Camera for Short and Medium Span Bridges

An accurate and robust monitoring method of full‐bridge traffic load distribution based on YOLO‐v3 machine vision

A robust bridge weigh‐in‐motion algorithm based on regularized total least squares with axle constraints

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