BWIM with constant and variable velocity: theoretical derivation

Algohi, Basheer; Mufti, Aftab A.; Bakht, Baidar

doi:10.1007/s13349-019-00377-0

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…Carraro et al (2019) compared the performances of several BWIM algorithms through numerical simulation and field testing. Algohi et al (2020) derived a modified influence area method for BWIM to consider the effect of variable vehicle speed. Kawakatsu et al (2019Kawakatsu et al ( , 2020 presented a "fully neural" BWIM method that uses onedimensional deep CNNs to learn the relationship between vehicle parameters and bridge responses from monitoring data.…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic vehicle weight estimation using physics‐constrained generative adversarial network

Cai

Liu

2021

Computer aided Civil Eng

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Traffic information plays an important role in the design and management of civil transportation infrastructure. Bridge weigh-in-motion (BWIM) provides an effective tool for traffic information gathering by estimating vehicle parameters including its weight through bridge responses. Most existing BWIM algorithms rarely consider the epistemic uncertainty of vehicle weight in terms of the probabilistic distribution of estimated axle weights (AWs) of the vehicle. This paper proposes a novel methodology for probabilistic vehicle weight estimation using a physics-constrained generative adversarial network (GAN). Generative models are introduced to describe the probabilistic distributions of estimated AWs and bridge responses. Physics constraints on the generative models are formulated and enforced by minimizing a physics-based loss function. The generative models are then learned by training a physics-constrained GAN using the observed bridge responses. Numerical study and field testing are conducted to demonstrate the proposed method using representative highway bridges and vehicles.The results show that the proposed method can successfully capture the uncertainty in the vehicle weight estimation and provide the probabilistic distributions of the estimated AWs for different vehicle types and loading conditions considered, which can enhance the application of BWIM for relevant tasks such as traffic data collection and truck overloading enforcement. Based on the results obtained from the numerical study and field testing, the maximum coefficient of variation obtained for the AWs and gross vehicle weight of the presented cases are 0.55 and 0.11, respectively.

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Section: Introductionmentioning

confidence: 99%

“…Algohi et al. (2020) derived a modified influence area method for BWIM to consider the effect of variable vehicle speed. Kawakatsu et al.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic vehicle weight estimation using physics‐constrained generative adversarial network

Cai

Liu

2021

Computer aided Civil Eng

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“…Therefore, to ensure the safety and improve the operation efficiency of highway bridges and other infrastructures, reliable traffic information needs to be monitored, and thus reasonable and feasible overloading countermeasures can be summed. Bridge weigh-in-motion (BWIM) technology, which efficiently estimates the axle weight of test vehicles from the response of instrumented bridges, is widely used nowadays [2][3][4][5][6][7]. Compared to traditional pavement-based weigh-in-motion method, a BWIM system is more durable and stable, since it uses sensors under the soffit of a bridge instead of placing them on the road surface.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking and Load Monitoring for Moving Vehicles on Bridge Based on Axle Position and Dual Camera Vision

Zhao

Deng

et al. 2021

Remote Sensing

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Monitoring traffic loads is vital for ensuring bridge safety and overload controlling. Bridge weigh-in-motion (BWIM) technology, which uses an instrumented bridge as a scale platform, has been proven as an efficient and durable vehicle weight identification method. However, there are still challenges with traditional BWIM methods in solving the inverse problem under certain circumstances, such as vehicles running at a non-constant speed, or multiple vehicle presence. For conventional BWIM systems, the velocity of a moving vehicle is usually assumed to be constant. Thus, the positions of loads, which are vital in the identification process, is predicted from the acquired speed and axle spacing by utilizing dedicated axle detectors (installed on the bridge surface or under the bridge soffit). In reality, vehicles may change speed. It is therefore difficult or even impossible for axle detectors to accurately monitor the true position of a moving vehicle. If this happens, the axle loads and bridge response cannot be properly matched, and remarkable errors can be induced to the influence line calibration process and the axle weight identification results. To overcome this problem, a new BWIM method was proposed in this study. This approach estimated the bridge influence line and axle weight by associating the bridge response and axle loads with their accurate positions. Binocular vision technology was used to continuously track the spatial position of the vehicle while it traveled over the bridge. Based on the obtained time–spatial information of the vehicle axles, the ordinate of influence line, axle load, and bridge response were correctly matched in the objective function of the BWIM algorithm. The influence line of the bridge, axle, and gross weight of the vehicle could then be reliably determined. Laboratory experiments were conducted to evaluate the performance of the proposed method. The negative effect of non-constant velocity on the identification result of traditional BWIM methods and the reason were also studied. Results showed that the proposed method predicted bridge influence line and vehicle weight with a much better accuracy than conventional methods under the considered adverse situations, and the stability of BWIM technique also was effectively improved. The proposed method provides a competitive alternative for future traffic load monitoring.

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Evaluation of a Load Cell-Based Bridge Weigh-in-Motion System

Shi,

Yarnold,

Hurlebaus

et al. 2024

Transportation Research Record: Journal of the Transportation R

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A bridge weigh-in-motion (B-WIM) system utilizes an instrumented bridge to obtain the axle information (weights, number, and spacings) and weight information (axle and gross) of vehicles that cross the structure. Traditional B-WIM systems are based on strain measurements and utilize the Moses algorithm. A wide array of additional measurement types and algorithms have been explored by researchers. However, load cells have rarely been used for B-WIM systems. This paper proposes a B-WIM system using load cells to measure the reaction force at the supports. Full-scale field tests were conducted on a slab-beam bridge instrumented with load cells to evaluate the effectiveness and accuracy of the proposed B-WIM system. The second derivative of the load cell data was used to accurately find the axle information of the trucks. Two weight calculation methods, the reaction force method and the area method, were utilized with the field test data. The results were then quantitatively compared. Overall, load cells show promise for use in future B-WIM systems given the reasonable accuracies determined in this study.

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BWIM with constant and variable velocity: theoretical derivation

Cited by 3 publications

References 7 publications

Probabilistic vehicle weight estimation using physics‐constrained generative adversarial network

Probabilistic vehicle weight estimation using physics‐constrained generative adversarial network

Trajectory Tracking and Load Monitoring for Moving Vehicles on Bridge Based on Axle Position and Dual Camera Vision

Evaluation of a Load Cell-Based Bridge Weigh-in-Motion System

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