Статті в журналах: "Wegh in Motion Systems"

1

Sonmez, Umit, Nina Sverdlova, Robin Tallon, David Klinikowski, and Donald Streit. "Static calibration methodology for weigh-in-motion systems." International Journal of Heavy Vehicle Systems 7, no. 2/3 (2000): 191. http://dx.doi.org/10.1504/ijhvs.2000.004836.

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2

Cebon, D. "Design of Multiple-Sensor Weigh-in-Motion Systems." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 204, no. 2 (April 1990): 133–44. http://dx.doi.org/10.1243/pime_proc_1990_204_145_02.

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3

Stawska, Sylwia, Jacek Chmielewski, Magdalena Bacharz, Kamil Bacharz, and Andrzej Nowak. "Comparative Accuracy Analysis of Truck Weight Measurement Techniques." Applied Sciences 11, no. 2 (January 14, 2021): 745. http://dx.doi.org/10.3390/app11020745.

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Roads and bridges are designed to meet the transportation demands for traffic volume and loading. Knowledge of the actual traffic is needed for a rational management of highway infrastructure. There are various procedures and equipment for measuring truck weight, including static and in weigh-in-motion techniques. This paper aims to compare four systems: portable scale, stationary truck weigh station, pavement weigh-in-motion system (WIM), and bridge weigh-in-motion system (B-WIM). The first two are reliable, but they have limitations as they can measure only a small fraction of the highway traffic. Weigh-in-motion (WIM) measurements allow for a continuous recording of vehicles. The presented study database was obtained at a location that allowed for recording the same traffic using all four measurement systems. For individual vehicles captured on a portable scale, the results were directly compared with the three other systems’ measurements. The conclusion is that all four systems produce the results that are within the required and expected accuracy. The recommendation for an application depends on other constraints such as continuous measurement, installation and operation costs, and traffic obstruction.

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4

Burnos, Piotr, Janusz Gajda, Ryszard Sroka, Monika Wasilewska, and Cezary Dolega. "High Accuracy Weigh-In-Motion Systems for Direct Enforcement." Sensors 21, no. 23 (December 1, 2021): 8046. http://dx.doi.org/10.3390/s21238046.

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In many countries, work is being conducted to introduce Weigh-In-Motion (WIM) systems intended for continuous and automatic control of gross vehicle weight. Such systems are also called WIM systems for direct enforcement (e-WIM). The achievement of introducing e-WIM systems is conditional on ensuring constant, known, and high-accuracy dynamic weighing of vehicles. WIM systems weigh moving vehicles, and on this basis, they estimate static parameters, i.e., static axle load and gross vehicle weight. The design and principle of operation of WIM systems result in their high sensitivity to many disturbing factors, including climatic factors. As a result, weighing accuracy fluctuates during system operation, even in the short term. The article presents practical aspects related to the identification of factors disturbing measurement in WIM systems as well as methods of controlling, improving and stabilizing the accuracy of weighing results. Achieving constant high accuracy in weighing vehicles in WIM systems is a prerequisite for their use in the direct enforcement mode. The research results presented in this paper are a step towards this goal.

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5

Mykyjchuk, Mykola, Taras Hut, and Nadiya Lazarenko. "METROLOGICAL REQUIREMENTS OF WEIGH-IN-MOTION SYSTEMS FOR VEHICLES." Measuring Equipment and Metrology 82, no. 2 (2021): 10–15. http://dx.doi.org/10.23939/istcmtm2021.02.010.

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The article analyzes and proposes solutions for metrological support of weight information systems of road vehicles in motion, including the method of classification of WIM systems by purpose and accuracy classes, metrological requirements for them and control methods for testing and verification, as well as the main metrological risks for Weigh-in-motion systems for road vehicles and requirements for determining and calculating reliability.

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6

Dyshenko, V. S., A. E. Raskutin, and M. A. Zuev. "The road detector in systems of Weigh-In-Motion." Proceedings of VIAM, no. 5 (2016): 12. http://dx.doi.org/10.18577/2307-6046-2016-0-5-12-12.

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7

Gajda, Janusz, Ryszard Sroka, and Piotr Burnos. "Designing the Calibration Process of Weigh-In-Motion Systems." Electronics 10, no. 20 (October 18, 2021): 2537. http://dx.doi.org/10.3390/electronics10202537.

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Weigh-In-Motion (WIM) systems provide information on the state of road traffic and are used in activities undertaken as part of traffic supervision and management, enforcement of applicable regulations, and in the design of road infrastructure. The further development of such systems is aimed at increasing their measurement accuracy, operational reliability, and their resistance to disturbing environmental factors. Increasing the accuracy of measurement can be achieved both through actions taken in the hardware layer (technology of load sensors, the number of sensors and their arrangement, technology used in the construction of the pavement, selection of the system location), as well as by implementing better system calibration algorithms and algorithms for pre-processing measurement data. In this paper, we focus on the issue of WIM system calibration. We believe that through the correct selection of the calibration algorithm, it is possible to significantly increase the accuracy of vehicle weighing in WIM systems, from a practical point of view. The simulation and experimental studies we conducted confirmed this hypothesis.

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8

Ryguła, Artur, Krzysztof Brzozowski, and Andrzej Maczyński. "Limitations of the effectiveness of Weigh in Motion systems." Open Engineering 10, no. 1 (March 17, 2020): 183–96. http://dx.doi.org/10.1515/eng-2020-0020.

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AbstractOverloaded vehicles pose a real threat to road safety and significantly contribute to the degradation of the road surface. High-Speed Weigh in Motion (HS-WIM) stations are the commonly used method of eliminating them from traffic. In Poland, HS-WIM stations operate in pre-selection mode, sending information to services about the potential exceedance of acceptable standards by a specific vehicle. The article presents the results of the data analysis from selected HS-WIM stations operating on the national road network in Poland indicating significant limitations of the effectiveness of the whole system. The main reason for this may be that carriers use the knowledge about the HS-WIM stations location and working time to avoid inspections. The results presented in the paper indicate, among other things, that in some locations the share of vehicles overloaded with traffic increases significantly outside the working hours of the controlling services. For Light Commercial Vehicle, the share of overloaded vehicles in this group is also significant. Also, the paper indicates that the effectiveness of the procedure for determining vehicle overload has been limited due to errors in classification.

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9

Mosleh, Araliya, Pedro Alves Costa, and Rui Calçada. "A new strategy to estimate static loads for the dynamic weighing in motion of railway vehicles." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 234, no. 2 (March 29, 2019): 183–200. http://dx.doi.org/10.1177/0954409719838115.

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The present paper focuses on the numerical modeling of a weigh-in-motion system developed with the purpose of assessing the static loads imposed by the train onto the track infrastructure. Weigh-in-motion systems would be useful in overcoming the disadvantages typical of the conventional static weighing such as costs and traffic management. However, contrary to the conventional static weighing, weigh-in-motion systems do not allow a direct measurement of the static load since the train–track dynamic interaction gives rise to dynamic loads that are added to the static ones. This study investigates how train speed and track unevenness affect the loads assessed by the weigh-in-motion system. In order to achieve that goal, a comprehensive statistical study was performed based on an extensive amount of calculations. Finally, based on the conclusions and trend identified through the comprehensive parametric study, an approach is proposed to correct the direct result given by the weigh-in-motion system in order to obtain an estimation of the static load.

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10

Gajda, Janusz, Ryszard Sroka, and Piotr Burnos. "Sensor Data Fusion in Multi-Sensor Weigh-In-Motion Systems." Sensors 20, no. 12 (June 13, 2020): 3357. http://dx.doi.org/10.3390/s20123357.

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In this paper, we present the results of a comparison of two estimators of the gross vehicle weight (GVW) and the static load of individual axles of vehicles. The estimators were used to process measurement data derived from Multi-Sensor Weigh-In-Motion systems (MS-WIM). The term estimator is understood as an algorithm according to which the dynamic axle load measurement results are processed in order to determine the static load. The result obtained is called static load estimate. As a measure of measurement uncertainty, we adopted the standard deviation of the static load estimate. The mean value and the maximum likelihood estimators were compared. Studies were conducted using simulation methods based on synthetic data and experimental data obtained from a WIM system equipped with 16 lines of polymer axle load sensors. We have shown a substantially lower uncertainty of estimates determined using the maximum likelihood estimator. The results obtained have considerable practical significance, particularly during long-term usage of multi-sensor WIM systems.

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11

O'Brien;, Eugene J., Ales Znidaric, and Anthony T. Dempsey. "Comparison of two independently developed bridge weigh-in-motion systems." International Journal of Heavy Vehicle Systems 6, no. 1/2/3/4 (1999): 147. http://dx.doi.org/10.1504/ijhvs.1999.054503.

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12

Petersen, DR, RE Link, and AT Papagiannakis. "Calibration of Weigh-in-Motion Systems Through Dynamic Vehicle Simulation." Journal of Testing and Evaluation 25, no. 2 (1997): 197. http://dx.doi.org/10.1520/jte11479j.

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13

Žnidarič, Aleš, Jan Kalin, and Maja Kreslin. "Improved accuracy and robustness of bridge weigh-in-motion systems." Structure and Infrastructure Engineering 14, no. 4 (December 2017): 412–24. http://dx.doi.org/10.1080/15732479.2017.1406958.

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14

Zhi, Xun, Ahmed Shalaby, Dan Middleton, and Alan Clayton. "Evaluation of weigh-in-motion in Manitoba." Canadian Journal of Civil Engineering 26, no. 5 (October 1, 1999): 655–66. http://dx.doi.org/10.1139/l99-025.

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The primary objective of a weigh-in-motion (WIM) system is to provide highway designers and agencies with information on the loads and traffic volumes using a particular highway, thereby facilitating improved pavement design, management, and weight enforcement. In this paper, the historic performance of WIM systems in Manitoba is evaluated. The results indicate that large numbers of unreasonable data are produced from the WIM systems, calibration procedures are not standardized, and there is drift in calibration. The performance of the Brokenhead WIM system was evaluated through a detailed survey conducted at the Brokenhead WIM site and the Westhawk Permanent Truck Weigh Station in August 1997. The Brokenhead site is on the Trans-Canada highway east of Winnipeg. It is the only WIM system in the country that measures truck characteristics and movements between eastern and western Canada. The survey produced a large database permitting the comparison of truck dimension measurements, truck weights, and vehicle classification between those produced by the WIM system and those observed manually. The results indicate that WIM axle-spacing data sets were outside the tolerance for 95% conformity specified by the American Society for Testing and Materials (ASTM). The system classified 5 to 9 axle combination trucks more accurately than some 2- and 3-axle vehicles. The WIM system underestimated about 90% of truck weights in the survey period. The degree of underestimation exceeded 50% of the corresponding static weights. This finding highlights the importance of quality control and corrections on WIM data prior to their use in research or engineering practice.Key words: weigh-in-motion, vehicle classification, calibration, axle spacing, axle load.

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15

Cantero, Daniel, Arturo González, and Biswajit Basu. "Monitoring of Changes in Bridge Response Using Weigh-In-Motion Systems." Key Engineering Materials 569-570 (July 2013): 183–90. http://dx.doi.org/10.4028/www.scientific.net/kem.569-570.183.

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Weigh-In-Motion (WIM) and Bridge Weigh-In-Motion (B-WIM) are systems that allow obtaining the axle weights of road vehicles in motion, at normal traffic speeds. While WIM employs sensors embedded in the road pavement, B-WIM use the strain recordings of a bridge to infer the traversing vehicle axle weights. Both systems have been heavily improved over the past decades, and commercial versions are currently in operation. The two main applications of these systems are: (1) to assess the traffic loading on the infrastructure, and (2) to enforce the maximum weight limits. This paper suggests a novel application of these two systems to identify changes in bridge stiffness. It requires the bridge to be instrumented with a B-WIM system and a WIM system nearby. The principle is to use both systems to evaluate the gross weight of vehicles passing over the bridge and correlate their predictions. Changes in correlation of the predicted axle weights over time will indicate either structural damage or faulty sensor. A finite element model of a coupled vehicle-bridge system with different damage scenarios is used to test the approach numerically. Vehicle mechanical properties and speeds are randomly sampled within a Monte Carlo simulation. Results show how correlation changes as damage increases and how this correlation can be employed as a damage indicator.

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16

Edalatmanesh, Rahman, and John P. Newhook. "Using search based optimization algorithms in Bridge Weigh-In-Motion systems." Bridge Structures 6, no. 3,4 (2010): 107–19. http://dx.doi.org/10.3233/brs-2010-012.

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17

Gajda, Janusz, Piotr Burnos, and Ryszard Sroka. "Accuracy Assessment of Weigh-in-Motion Systems for Vehicle's Direct Enforcement." IEEE Intelligent Transportation Systems Magazine 10, no. 1 (2018): 88–94. http://dx.doi.org/10.1109/mits.2017.2776111.

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18

Žnidarič, A., G. Turk, and E. Zupan. "Determination of strain correction factors for bridge weigh-in-motion systems." Engineering Structures 102 (November 2015): 387–94. http://dx.doi.org/10.1016/j.engstruct.2015.08.026.

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19

Mihaila, Marius, Paul Barsanescu, and Ciprian Moraras. "Weigh-in-Motion Sensors and Traffic Monitoring Systems. State of the Art and Perspectives." Bulletin of the Polytechnic Institute of Iași. Machine constructions Section 68, no. 1 (March 1, 2022): 125–46. http://dx.doi.org/10.2478/bipcm-2022-0010.

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Abstract Weigh-in-motion (WIM) sensors allow the control of vehicle weights without disruption of traffic. By monitoring traffic and by reducing the number of overweight vehicles, the WIM sensors bring very important savings. This paper discusses the present status and developmental trends of weigh-in-motion (WIM) technologies. Both commercial and new types of WIM sensors are presented. Strengths and weaknesses of different type of WIM sensors are discussed. It is also presented the tendency to equip the WIM systems with different types of sensors, in order to evaluate other effects: reducing the fuel consumption, emission of pollutants, noise and vibrations, etc. Possible trends for the further development of WIM sensors are anticipated.

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20

Benekohal, Rahim F., Yoassry M. El-Zohairy, and Stanley Wang. "Truck Travel Time Around Weigh Stations: Effects of Weigh in Motion and Automatic Vehicle Identification Systems." Transportation Research Record: Journal of the Transportation Research Board 1716, no. 1 (January 2000): 135–43. http://dx.doi.org/10.3141/1716-16.

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Weigh in motion (WIM) technology may provide an efficient and cost-effective complement to static weighing. An evaluation of the effectiveness of an automated bypass system around a weigh station in Illinois is presented. The system combines the use of automatic vehicle identification (AVI), high-speed weigh in motion (HSWIM), and low-speed weigh in motion (LSWIM) technologies to facilitate preclearance for trucks at the weigh station. The preinstallation conditions were compared with post-installation conditions of WIM/AVI so that the effects and benefits of the system could be evaluated. During preinstallation, average delay was 4.9 min/truck, and 7 percent of trucks had delays of more than 10 min. The station was intermittently closed to prevent the truck queue from backing up onto the Interstate highway, allowing 15 to 51 percent of trucks to bypass the station without being weighed. In postinstallation, the delay for trucks equipped with transponder and allowed to bypass on the freeway was reduced by 4.17 min. The delay for trucks equipped with transponders and allowed to bypass inside the weigh station was reduced by 2.02 min. The delay for trucks that reported to the weigh station decreased by 1.25 min. On the other hand, less than 1 percent of trucks that have been observed in after-study were able to bypass on the freeway. With greater numbers of trucks being checked, fewer trucks on the road may exceed the allowable weight limits. Consequently, electronic screening minimizes road deterioration and risks to public safety and levels the playing field for illegally operating carriers and carriers who operate in compliance with the law.

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21

Rys, Dawid. "Investigation of Weigh-in-Motion Measurement Accuracy on the Basis of Steering Axle Load Spectra." Sensors 19, no. 15 (July 25, 2019): 3272. http://dx.doi.org/10.3390/s19153272.

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Weigh-in-motion systems are installed in pavements or on bridges to identify and reduce the number of overloaded vehicles and minimise their adverse effect on road infrastructure. Moreover, the collected traffic data are used to obtain axle load characteristics, which are very useful in road infrastructure design. Practical application of data from weigh-in-motion has become more common recently, which calls for adequate attention to data quality. This issue is addressed in the presented paper. The aim of the article is to investigate the accuracy of 77 operative weigh-in-motion stations by analysing steering axle load spectra. The proposed methodology and analysis enabled the identification of scale and source of errors that occur in measurements delivered from weigh-in-motion systems. For this purpose, selected factors were investigated, including the type of axle load sensor, air temperature and vehicle speed. The results of the analysis indicated the obvious effect of the axle load sensor type on the measurement results. It was noted that systematic error increases during winter, causing underestimation of axle loads by 5% to 10% for quartz piezoelectric and bending beam load sensors, respectively. A deterioration of system accuracy is also visible when vehicle speed decreases to 30 km/h. For 25% to 35% of cases, depending on the type of sensor, random error increases for lower speeds, while it remains at a constant level at higher speeds. The analysis also delivered a standard steering axle load distribution, which can have practical meaning in the improvement of weigh-in-motion accuracy and traffic data quality.

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22

Žnidarič, Aleš, and Jan Kalin. "Using bridge weigh-in-motion systems to monitor single-span bridge influence lines." Journal of Civil Structural Health Monitoring 10, no. 5 (July 31, 2020): 743–56. http://dx.doi.org/10.1007/s13349-020-00407-2.

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Abstract Bridge weigh-in-motion systems use instrumented bridges or culverts to weigh vehicles as they pass over the structures. They also provide data to allow the calculation of several bridge performance indicators. The article starts with the basics of a bridge weigh-in-motion system and briefly describes two key bridge performance indicators, girder distribution factor and dynamic amplification factor, which are also derived from B-WIM measurements. The central part of the article focuses on monitoring of influence lines, the third key parameter that characterises the bridge performance under traffic loads. First, the method of calculating the bending moment influence lines from random heavy traffic is described. A coefficient of rotational stiffness is introduced, which defines the shape of influence lines around the supports as a linear combination of the ideal simply supported and fixed supported influence lines, to allow quantifying the influence line changes. Then the long-term monitoring of influence lines is investigated on four different single-span test bridges. The initial focus is given on the examination of the effect of temperature on the shape of influence lines. Finally, two sets of influence lines are compared on one test bridge, one from before and the other from after replacing the expansion joints and bearings. The work done so far confirms that calculating of influence lines from random vehicles with a B-WIM system is entirely feasible and that differences in their shape can be detected on single-span bridges. What remains to be investigated is the comparison of these differences to the actual damages and under which circumstances the proposed procedure can compete with or better the routine bridge inspection and the conventional monitoring techniques.

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23

Burnos, Piotr, and Janusz Gajda. "Optimised Autocalibration Algorithm of Weigh-In-Motion Systems for Direct Mass Enforcement." Sensors 20, no. 11 (May 27, 2020): 3049. http://dx.doi.org/10.3390/s20113049.

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Dynamic vehicle weighing systems, also known as Weigh-In-Motion (WIM), are sensitive to factors which interfere with the measurement, including weather and climate conditions. This is a result of the sensitivity of the axle load sensors used in the systems. As a result, a significant change in the precision of weighing can be observed over short periods of time (even less than 1 h). This fact is a deterrent to the use of such systems for direct mass enforcement. In this article, we present a solution for this problem using an optimised autocalibration algorithm. We show the results of simulation studies which we conducted on the proposed algorithm. These were then verified experimentally at an in-road site. We demonstrated that autocalibration of the WIM system allows for effective limitation of the sensitivity of weighing results to interfering factors. This is, however, conditioned on a sufficiently high frequency of reference vehicles crossing the WIM site. The required frequency depends on the speed of change in the concentration of influencing factors.

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Gajda, Janusz, Ryszard Sroka, Tadeusz Zeglen, and Piotr Burnos. "The Influence of Temperature on Errors of Wim Systems Employing Piezoelectric Sensors Keywords: Piezoelectric Sensors, Temperature Influence, Temperature Error Of Wim Systems, Error Correction." Metrology and Measurement Systems 20, no. 2 (June 1, 2013): 171–82. http://dx.doi.org/10.2478/mms-2013-0015.

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Abstract The paper provides analysis of the influence of temperature on the error of weigh-in-motion (WIM) systems utilizing piezoelectric polymer load sensors. Results of tests of these sensors in a climatic chamber, as well as results of long-term tests at the WIM site, are presented. Different methods for correction of the influence of changes in temperature were assessed for their effectiveness and compared.

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Iaquinta, J., E. Merliot, L.-M. Cottineau, and J.-P. Desroche. "Piezoelectric Sensors for Weigh-In-Motion Systems: An Experimental Insight into Edge Effects." Journal of Testing and Evaluation 32, no. 6 (2004): 12207. http://dx.doi.org/10.1520/jte12207.

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He, Wei, Tianyang Ling, Eugene J. OBrien, and Lu Deng. "Virtual Axle Method for Bridge Weigh-in-Motion Systems Requiring No Axle Detector." Journal of Bridge Engineering 24, no. 9 (September 2019): 04019086. http://dx.doi.org/10.1061/(asce)be.1943-5592.0001474.

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Lydon, Myra, D. Robinson, S. E. Taylor, G. Amato, E. J. O. Brien, and N. Uddin. "Improved axle detection for bridge weigh-in-motion systems using fiber optic sensors." Journal of Civil Structural Health Monitoring 7, no. 3 (July 2017): 325–32. http://dx.doi.org/10.1007/s13349-017-0229-4.

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Suo, Chun Guang, Wen Bin Zhang, and Feng Jie Yang. "Weigh-in-Motion to Estimate Vehicle Weight of Rigid Pavement Strain Signal." Advanced Materials Research 655-657 (January 2013): 786–89. http://dx.doi.org/10.4028/www.scientific.net/amr.655-657.786.

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The aim of this research is to investigate the feasibility of developing a traffic load detector system for the purpose of accurate weighing vehicle to aid traffic management systems. The possibility of estimate the velocity, axle and gross weight of a moving vehicle from the surface of pavement tensile strain is investigated. The objective is to develop weigh-in-motion (WIM) technique to estimate the vehicle weight from the pavement responses with fewer disturbances to the vehicle’s vibration and traveling speed, and to provide more reliable and durable WIM systems and sensors. The repeatability, sources of error, and accuracy of weight prediction were discussed.

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Qin, Tianhao, Mengxiang Lin, Ming Cao, Kaiya Fu, and Rong Ding. "Effects of Sensor Location on Dynamic Load Estimation in Weigh-in-Motion System." Sensors 18, no. 9 (September 12, 2018): 3044. http://dx.doi.org/10.3390/s18093044.

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In recent years, weigh-in-motion systems based on embedded sensor networks have received a lot of attention. However, how to improve the accuracy of multi-sensor weigh-in-motion (WIM) systems while keeping costs low remains a challenge. In this paper, a numerical simulation method is presented to analyze the relationship between sensor location and the accuracy of static weight estimation. The finite element model of a WIM system is developed, which consists of three parts: a pavement model, a moving load model and two types of sensor models. Analysis of simulation results shows that the ability of sensing dynamic load is closely related to the installation depth of sensors and pavement material. Moreover, the distance between the moving wheel and sensors has a great impact on estimating performance. Gaussian curve fitting could be used to reduce weighing error within a limited range. Our work suggests that much more attention should be paid to the design of the sensor layout of a WIM system.

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JUNGES, P., R. C. A. PINTO, and L. F. FADEL MIGUEL. "B-WIM systems application on reinforced concrete bridge structural assessment and highway traffic characterization." Revista IBRACON de Estruturas e Materiais 10, no. 6 (November 2017): 1338–65. http://dx.doi.org/10.1590/s1983-41952017000600010.

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Abstract The vehicles that travel on Brazilian highways have changed a lot in the last decades, with an increase in the traffic load and in the amount of trucks. This fact is not exclusive to our country, so much that in order to assess the structural safety of bridges, there was a great development in bridge weigh-in-motion systems (B-WIM) the last decade, especially in developed countries. Moses, in 1979, was the first one to introduce the B-WIM concept. This work presents the results of a B-WIM system applied on a bridge over the Lambari river, located at BR 153 in Uruaçu (Goiás). The weigh-in-motion technique used is based on Moses' Algorithm and uses influence lines obtained direct from traffic. Traffic characterization of that particular highway, as well as the effects introduced in the bridge structure and the experimental dynamic amplification factor are also discussed. At the end it is concluded that the system used is capable of detecting, with good precision, the axle spacing and the gross vehicle weight shows errors inferior to 3% when compared with the gross weight acquired with static scale.

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31

Pintão, Bruno, Araliya Mosleh, Cecilia Vale, Pedro Montenegro, and Pedro Costa. "Development and Validation of a Weigh-in-Motion Methodology for Railway Tracks." Sensors 22, no. 5 (March 3, 2022): 1976. http://dx.doi.org/10.3390/s22051976.

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In railways, weigh-in-motion (WIM) systems are composed of a series of sensors designed to capture and record the dynamic vertical forces applied by the passing train over the rail. From these forces, with specific algorithms, it is possible to estimate axle weights, wagon weights, the total train weight, vehicle speed, etc. Infrastructure managers have a particular interest in identifying these parameters for comparing real weights with permissible limits to warn when the train is overloaded. WIM is also particularly important for controlling non-uniform axle loads since it may damage the infrastructure and increase the risk of derailment. Hence, the real-time assessment of the axle loads of railway vehicles is of great interest for the protection of railways, planning track maintenance actions and for safety during the train operation. Although weigh-in-motion systems are used for the purpose of assessing the static loads enforced by the train onto the infrastructure, the present study proposes a new approach to deal with the issue. In this paper, a WIM algorithm developed for ballasted tracks is proposed and validated with synthetic data from trains that run in the Portuguese railway network. The proposed methodology to estimate the wheel static load is successfully accomplished, as the load falls within the confidence interval. This study constitutes a step forward in the development of WIM systems capable of estimating the weight of the train in motion. From the results, the algorithm is validated, demonstrating its potential for real-world application.

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32

Trischuk, Derek, Curtis Berthelot, and Brian Taylor. "Weigh-in-Motion Applications for Intelligent Transportation Systems-Commercial Vehicle Operations: Evaluation Using WESTA." Transportation Research Record: Journal of the Transportation Research Board 1816, no. 1 (January 2002): 87–95. http://dx.doi.org/10.3141/1816-10.

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33

Kalhori, Hamed, Mehrisadat Makki Alamdari, Xinqun Zhu, Bijan Samali, and Samir Mustapha. "Non-intrusive schemes for speed and axle identification in bridge-weigh-in-motion systems." Measurement Science and Technology 28, no. 2 (January 11, 2017): 025102. http://dx.doi.org/10.1088/1361-6501/aa52ec.

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34

Marković, Nikola, Ilya O. Ryzhov, and Paul Schonfeld. "Evasive flow capture: Optimal location of weigh-in-motion systems, tollbooths, and security checkpoints." Networks 65, no. 1 (November 27, 2014): 22–42. http://dx.doi.org/10.1002/net.21581.

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35

Hamad, Ahmed A., Yasseen Sadoon Atiya, and Hilal Al-Libawy. "A new approach for varied speed weigh-in-motion vehicle based on smartphone inertial sensors." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 4 (December 1, 2022): 1554. http://dx.doi.org/10.11591/ijai.v11.i4.pp1554-1562.

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Анотація:

<p>Dynamic vehicle weight measuring, weigh-in-motion (WIM), is an important metric that can reflect significantly vehicle driving behaviour and in turn, it will affect both safety and traffic status. Several accurate WIM systems are developed and implemented successfully. These systems are using under road weighing sensor which are costly to implement. Moreover, it is costly and not very practical to embed a continuous weighing system in used cars. In this work, a low-cost varied-speed weigh-in-motion approach was suggested to continuously measuring vehicle load based on the response of smartphone sensors which is a reflection of vehicle dynamics. This approach can apply to any moving vehicle at any driving speed without the need for extra added hardware which makes it very applicable because smartphone is widely used device. The approach was tested through a six-trips experiment. Three capacities of load had been designed in this approach to be classified using a neural network classifier. The classification performance metrics are calculated and show an accuracy of 91.2%. This accuracy level is within error limits of existing WIM systems especially for high speed and proved the success of the suggested approach.</p>

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36

Hekič, Doron, Andrej Anžlin, Maja Kreslin, Aleš Žnidarič, and Peter Češarek. "Model Updating Concept Using Bridge Weigh-in-Motion Data." Sensors 23, no. 4 (February 12, 2023): 2067. http://dx.doi.org/10.3390/s23042067.

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Finite element (FE) model updating of bridges is based on the measured modal parameters and less frequently on the measured structural response under a known load. Until recently, the FE model updating did not consider strain measurements from sensors installed for weighing vehicles with bridge weigh-in-motion (B-WIM) systems. A 50-year-old multi-span concrete highway viaduct, renovated between 2017 and 2019, was equipped with continuous monitoring system with over 200 sensors, and a B-WIM system. In the most heavily instrumented span, the maximum measured longitudinal strains induced by the full-speed calibration vehicle passages were compared with the modelled strains. Based on the sensitivity study results, three variables that affected its overall stiffness were updated: Young’s modulus adjustment factor of all structural elements, and two anchorage reduction factors that considered the interaction between the superstructure and non-structural elements. The analysis confirmed the importance of the initial manual FE model updating to correctly reflect the non-structural elements during the automatic nonlinear optimisation. It also demonstrated a successful use of pseudo-static B-WIM loading data during the model updating process and the potential to extend the proposed approach to using random B-WIM-weighed vehicles for FE model updating and long-term monitoring of structural parameters and load-dependent phenomena.

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37

Ott, W. C., and A. T. Papagiannakis. "Weigh-in-Motion Data Quality Assurance Based on 3-S2 Steering Axle Load Analysis." Transportation Research Record: Journal of the Transportation Research Board 1536, no. 1 (January 1996): 12–18. http://dx.doi.org/10.1177/0361198196153600102.

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An innovative method is offered for conducting the quality assurance of weigh-in-motion (WIM) data by analyzing the variation in the measurements of five-axle semitrailer (3-S2) truck steering-axle loads. Confidence-interval limits for those measurements are established on the basis of their historic mean static loads adjusted for the effect of air resistance and their combined variation from two sources, the variation within a fleet of 3-S2 trucks and the variation due to axle dynamics at a WIM site. The first variation was determined by analyzing historic 3-S2 load data obtained by FHWA using static scales. The second variation was established through dynamic vehicle simulations of a “typical” 3-S2 truck using vehicle model VESYM and the roughness profile at a particular WIM site. The method was tested with data from four WIM systems in the state of Washington, two bending-plate systems and two piezoelectric systems. The proposed method resulted in clear indications of the performance of the scale versus time by the number of confidence interval violations observed.

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38

Kirushanth, Sivaramalingam, and Boniface Kabaso. "Design and Development of Weigh-In-Motion Using Vehicular Telematics." Journal of Sensors 2020 (March 4, 2020): 1–22. http://dx.doi.org/10.1155/2020/7871215.

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Identifying overloaded vehicles on a highway is essential for the safety of vehicles on the road as well as for the performance monitoring of highway infrastructure and planning. Traffic enforcement uses various weigh-in-motion (WIM) methods. Since Vehicular Telematics (VT) is favoured in the transport industry, using it for building a new WIM system to infer the payload of a vehicle at any road segment would be beneficial for the transport industry. This paper presents the effort taken to use VT data from onboard diagnostics modules and smartphones to infer the payload of a vehicle. The experiment done to find the correlation between VT data and the payload of a vehicle is discussed. Feature engineering was done; nine different settings were tested to find the best regression model. A multiple nonlinear regression model produced significant a p value of 6.322e-08 and an R-squared value of 0.8736. Results support the notion of using the VT data for nonintrusive measurement of the weight of a vehicle in motion.

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39

González, Arturo, A. Thomas Papagiannakis, and Eugene J. O’Brien. "Evaluation of an Artificial Neural Network Technique Applied to Multiple-Sensor Weigh-in-Motion Systems." Transportation Research Record: Journal of the Transportation Research Board 1855, no. 1 (January 2003): 151–59. http://dx.doi.org/10.3141/1855-19.

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Weigh-in-motion (WIM) accuracy in measuring static axle loads is affected by vehicle dynamics and noise. Neural networks can identify underlying relationships, such as the spatial repeatability in axle dynamics, and can efficiently remove noise. Furthermore, they can adapt to changing circumstances (e.g., traffic characteristics, road profile, or sensor failure), unlike conventional WIM calibration algorithms. The paper performance of a multilayer feed-forward artificial neural network algorithm applied to a multiple-sensor WIM is analyzed. Numerical simulations of the axle forces applied on a smooth road profile are used to train, validate, and test the artificial neural network algorithm. This dynamic axle load variation is predicted with the vehicle simulation model VESYM. The mechanical parameters of the truck models and their speeds are randomly varied over a range established from real traffic measurements. Once the theoretical WIM data are obtained at the sensor locations, the measurements are artificially corrupted with noise up to the typical level of WIM accuracy. Details are given on the process of the neural network design, the size of the training sample, and the length of the training period. The artificial neural networks approach resulted in higher accuracy than the traditional average-based calibration method, especially at high noise levels. As a result, it shows promise for estimating static axle loads from multiple WIM measurements.

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40

Jacob, Bernard, Eugene J. O'Brien, and W. Newton. "Assessment of the accuracy and classification of weigh-in-motion systems. Part 2: European specification." International Journal of Heavy Vehicle Systems 7, no. 2/3 (2000): 153. http://dx.doi.org/10.1504/ijhvs.2000.004831.

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41

Jacob, Bernard. "Assessment of the accuracy and classification of weigh-in-motion systems Part 1: Statistical background." International Journal of Heavy Vehicle Systems 7, no. 2/3 (2000): 136. http://dx.doi.org/10.1504/ijhvs.2000.004860.

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42

Dontu, A. I., P. D. Barsanescu, L. Andrusca, and N. A. Danila. "Weigh-in-motion sensors and traffic monitoring systems - Sate of the art and development trends." IOP Conference Series: Materials Science and Engineering 997 (December 25, 2020): 012113. http://dx.doi.org/10.1088/1757-899x/997/1/012113.

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43

Nassiri, Somayeh, Alireza Bayat, and Peter Kilburn. "Traffic inputs for mechanistic-empirical pavement design guide using weigh-in-motion systems in Alberta." International Journal of Pavement Engineering 15, no. 6 (May 17, 2013): 483–94. http://dx.doi.org/10.1080/10298436.2013.797978.

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44

Mahmoudabadi, Abbas, and Seyed Mohammad Seyedhosseini. "Improving the efficiency of weigh in motion systems through optimized allocating truck checking oriented procedure." IATSS Research 36, no. 2 (March 2013): 123–28. http://dx.doi.org/10.1016/j.iatssr.2012.08.002.

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45

Mardare, Irina, and Irina Tiţa. "Researches Regarding the Influence of Temperature on a Weigh-in-Motion Hydraulic System." Applied Mechanics and Materials 657 (October 2014): 679–83. http://dx.doi.org/10.4028/www.scientific.net/amm.657.679.

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Weighing in motion systems, regardless the type of sensor that a system utilizes is working in difficult conditions: dust, slag, layer of ice or snow, high humidity, varying temperatures during a day, or a year [. All these conditions influence the accuracy measures of the work done and require the correction factors application.In this paper it presents the analysis of the temperature influence on a new type of weighing in motion sensor, namely hydraulic sensor. Is presented the simulation scheme designed to simulate the experimental model used in the laboratory and the results obtained through simulation, on the temperature influence on the experimental model tested.

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46

Warscotte, Loïc, Jehan Boreux, Adriana Antofie, and Dominique Corbaye. "Direct Enforcement in Belgium with High Speed Weigh-in-Motion (HS-WIM)." Electronics 12, no. 3 (January 21, 2023): 555. http://dx.doi.org/10.3390/electronics12030555.

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Interest for high speed weigh-in-motion of vehicles, or HS-WIM keeps growing worldwide. The main purpose of such systems is checking weights of vehicles in a self manner, in order to impose a penalty to overloaded ones. Overloaded vehicles may cause several problems such as safety issues, road deterioration or unfair competition. Walloon Public Service (WPS) has dealt with the settings and approval of a HS-WIM system in Belgium. The latest resurfacing of the roadway around the prototype provides good-quality data that allows for reaching excellent results in terms of accuracy of the weight estimation. Therefore, direct weight enforcement may be activated since the system has a type approval certificate to be used in accordance with legal metrology requirements.

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47

Zhao, Shuanfeng, Jianwei Yang, Zenghui Tang, Qing Li, and Zhizhong Xing. "Methodological Study on the Influence of Truck Driving State on the Accuracy of Weigh-in-Motion System." Information 13, no. 3 (March 3, 2022): 130. http://dx.doi.org/10.3390/info13030130.

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The weigh-in-motion (WIM) system weighs the entire vehicle by identifying the dynamic forces of each axle of the vehicle on the road. The load of each axle is very important to detect the total weight of the vehicle. Different drivers have different driving behaviors, and when large trucks pass through the weighing detection area, the driving state of the trucks may affect the weighing accuracy of the system. This paper proposes YOLOv3 network model as the basis for this algorithm, which uses the feature pyramid network (FPN) idea to achieve multi-scale prediction and the deep residual network (ResNet) idea to extract image features, so as to achieve a balance between detection speed and detection accuracy. In the paper, spatial pyramid pooling (SPP) network and cross stage partial (CSP) network are added to the original network model to improve the learning ability of the convolutional neural network and make the original network more lightweight. Then the detection-based target tracking method with Kalman filtering + RTS (rauch–tung–striebel) smoothing is used to extract the truck driving status information (vehicle trajectory and speed). Finally, the effective size of the vehicle in different driving states on the weighing accuracy is statistically analyzed. The experimental results show that the method has high accuracy and real-time performance in truck driving state extraction, can be used to analyze the influence of weighing accuracy, and provides theoretical support for personalized accuracy correction of WIM system. At the same time, it is beneficial for WIM system to assist the existing traffic system more accurately and provide a highway health management and effective decision making by providing reliable monitoring data.

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48

Xu, Suan, Xing Chen, Yaqiong Fu, Hongwei Xu, and Kaixing Hong. "Research on Weigh-in-Motion Algorithm of Vehicles Based on BSO-BP." Sensors 22, no. 6 (March 9, 2022): 2109. http://dx.doi.org/10.3390/s22062109.

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Weigh-in-motion (WIM) systems are used to measure the weight of moving vehicles. Aiming at the problem of low accuracy of the WIM system, this paper proposes a WIM model based on the beetle swarm optimization (BSO) algorithm and the error back propagation (BP) neural network. Firstly, the structure and principle of the WIM system used in this paper are analyzed. Secondly, the WIM signal is denoised and reconstructed by wavelet transform. Then, a BP neural network model optimized by BSO algorithm is established to process the WIM signal. Finally, the predictive ability of BP neural network models optimized by different algorithms are compared and conclusions are drawn. The experimental results show that the BSO-BP WIM model has fast convergence speed, high accuracy, the relative error of the maximum gross weight is 1.41%, and the relative error of the maximum axle weight is 6.69%.

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49

Oh, Min Soo, Soon Min Kwon, and Cheol Oh. "Analyzing the Impact of Weigh-in-Motion (WIM)-based Overloading Enforcement Systems on Freeway Traffic Stream." International Journal of Highway Engineering 20, no. 5 (October 31, 2018): 129–40. http://dx.doi.org/10.7855/ijhe.2018.20.5.129.

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50

Ventura, Roberto. "Bridge’s vehicular loads characterization through Weight-In-Motion (WIM) systems. The case study of Brescia." European Transport/Trasporti Europei, no. 90 (February 2023): 1–12. http://dx.doi.org/10.48295/et.2023.90.6.

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The growing traffic flow and the increase in transported masses negatively affect infrastructural safety. Several authors have characterized traffic loads on bridges in the American and Chinese context using Weigh-in-Motion (WIM) systems. Conversely, very few studies have been carried out in Europe and, as far as the authors know, none in Italy. This study covers this gap by providing a statistical analysis of raw WIM data collected on a main bridge near the city of Brescia (Italy). First, the traffic flow and the characteristics of vehicles were gathered by a WIM device. Second, some descriptive statistics were performed by computing the probabilistic distributions of numerous vehicular attributes. Third, as a novelty element, a K-means based Clustering technique was adopted on a wide set of vehicular features to detect heavy vehicle clusters. The results showed the existence of three main clusters: two predominately composed by lightly overloaded ordinary vehicles and construction machinery, respectively, and one by mass exceptional vehicles. This study considers a broader set of vehicular parameters than previous ones and then, provides a deeper understanding. Moreover, it shows that axle mass limits violations are noteworthy among mass exceptional vehicles in Italy highlighting the need of improving weight enforcement. These knowledges will be crucial for a rational organisation of the existing assets.

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