Improvement of vehicle axle load test method based on portable WIM

Ji, Shaobo; Wang, Rongxu; Shu, Minglei; Han, Wenyang; Lan, Xin; Xu, Wei; Yin, Wei; Cheng, Yong

doi:10.1016/j.measurement.2020.108626

Cited by 11 publications

(5 citation statements)

References 21 publications

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“…Embedding sensors in roads, using their electromechanical characteristics for traffic monitoring: traffic flow detection, speed detection, and dynamic weighing measurement [171,172], these embedded sensors system are expected to reducing cable layout and maintenance when integrated energy harvesting system. With the development of new materials and technologies, sensing equipment can directly convert the effect of traffic participants on the infrastructure into electrical signals with characteristics, enabling the infrastructure to have capabilities.…”

Section: Self-powered Traffic Control Technologymentioning

confidence: 99%

Mechanical energy harvesting in traffic environment and its application in smart transportation

Xiao

Chang

et al. 2023

J. Phys. D: Appl. Phys.

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The concept of green and sustainable development is driving the convergence of transportation systems and energy technologies. New energy harvesting technology is an important way of the development in the green intelligent transportation system. Comparing with the power supply via batteries or cables, it has the advantages of convenient, sustainable, green and low carbon to harvest mechanical energy from the traffic environment and convert it into electrical energy to power the widely distributed small electromechanical systems. There are many studies on mechanical energy harvesting in traffic environment, few of them have comprehensively discussed these studies and their applications in the intelligent transportation. This paper first outlines the principles, methods, and energy management strategies of the mechanical energy harvesting in the traffic environment. The advantages, disadvantages, and applicability of various energy harvesting technologies are comprehensively and systematically analyzed from vehicle and road dimensions. The applications of energy harvesting technology was discussed includes: self-powered traffic control, self-powered vehicle-road collaboration and self-powered health monitoring of traffic infrastructure. Finally, the challenges and prospects of mechanical energy harvesting technology and applications in the traffic environment are discussed. Mechanical energy harvesting in traffic environment has broad application prospects in intelligent transportation, while improving the output power and reliability of the energy harvesting system is the key to its wide application in intelligent transportation systems.

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Section: Self-powered Traffic Control Technologymentioning

confidence: 99%

Mechanical energy harvesting in traffic environment and its application in smart transportation

Xiao

Chang

et al. 2023

J. Phys. D: Appl. Phys.

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“…Current research on vehicle weighing in motion (WIM) systems is mostly concentrated on heavy trucks, e.g., [ 4 , 5 , 6 , 7 ], because they significantly damage the road surface. These vehicles are next driven to certify static weigh stations [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Load Estimation of Moving Passenger Cars Using Inductive-Loop Technology

Marszałek

Duda

Piwowar

et al. 2023

Sensors

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Due to their lack of driving controllability, overweight vehicles are a big threat to road safety. The proposed method for a moving passenger car load estimation is capable of detecting an overweight vehicle, and thus it finds its application in road safety improvement. The weight of a car’s load entering or leaving a considered zone, e.g., industrial facility, a state, etc., is also of concern in many applications, e.g., surveillance. Dedicated vehicle weight-in-motion measurement systems generally use expensive load sensors that also require deep intervention in the road while being installed and also are calibrated only for heavy trucks. In this paper, a vehicle magnetic profile (VMP) is used for defining a load parameter proportional to the passenger vehicle load. The usefulness of the proposed load parameter is experimentally demonstrated in field tests. The sensitivity of the VMP to the load change results from the fact that the higher load decreases the vehicle clearance value which in turn increases the VMP. It is also shown that a slim inductive-loop sensors allows the building of a load estimation system, with a maximum error around 30 kg, which allows approximate determination of the number of passengers in the car. The presented proof of concept extends the functionality of inductive loops, already installed in the road, for acquiring other traffic parameters, e.g., moving vehicle axle-to-axle distance measurement, to road safety and surveillance related applications.

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“…Site-specific information of moving vehicles is very desirable for the design of new bridges as well as the evaluation of existing bridges (Ding et al, 2015; He et al, 2019a; Li et al, 2013). However, the calculation or direct measurement of the moving force is usually subjected to bias (Ji et al, 2020; Zhu et al, 2006). Force reconstruction and force identification techniques have been developed to fight this drawback, typically through solving an inverse problem on the basis of the measured dynamic responses of the bridge (Richardson et al, 2014; Weng et al, 2014; Yang and Yang, 2018; Yang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Modified truncated singular value decomposition method for moving force identification

Chen

Deng

Kong

2022

Advances in Structural Engineering

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In this study, a modified truncated singular value decomposition (MTSVD) method is proposed for the identification of dynamic moving forces on simply-supported beams. By regularizing the truncated singular value decomposition (TSVD) method, the MTSVD method focuses on overcoming the ill-posed problems that intrinsically exist in moving force identification. Two regularization parameters, namely, regularization matrix and truncating point are the most important regularization parameters affecting the performance of the MTSVD method. The accuracy and efficiency of the MTSVD method is shown by comparing the results with the conventional counterpart SVD and TSVD methods. In addition, the proposed method is also compared with a similar method recently proposed by the author, that is, the piecewise polynomial truncated singular value decomposition (PP-TSVD) method. Numerical simulations demonstrate that the performance of the MTSVD method is significantly improved compared with the PP-TSVD method in high noise level cases.

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Improvement of vehicle axle load test method based on portable WIM

Cited by 11 publications

References 21 publications

Mechanical energy harvesting in traffic environment and its application in smart transportation

Mechanical energy harvesting in traffic environment and its application in smart transportation

Load Estimation of Moving Passenger Cars Using Inductive-Loop Technology

Modified truncated singular value decomposition method for moving force identification

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