Bridge Weigh in Motion: Challenges and Opportunities

2021 ◽  
Author(s):  
Xiangang Lai
Keyword(s):  
Weigh In Motion ◽  
Challenges And Opportunities ◽  
Bridge Weigh In Motion

scholarly journals Comparative Accuracy Analysis of Truck Weight Measurement Techniques

2021 ◽  
Vol 11 (2) ◽  
pp. 745
Author(s):  
Sylwia Stawska ◽  
Jacek Chmielewski ◽  
Magdalena Bacharz ◽  
Kamil Bacharz ◽  
Andrzej Nowak
Keyword(s):  
Measurement Techniques ◽  
Highway Traffic ◽  
Highway Infrastructure ◽  
Measurement Systems ◽  
Motion System ◽  
Weigh In Motion ◽  
Comparative Accuracy ◽  
Bridge Weigh In Motion ◽  
Rational Management ◽  
Truck Weight

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.

Study on bridge weigh in motion (BWIM) system for measuring the vehicle parameters based on strain measurement using FBG sensors

2021 ◽  
Vol 61 ◽  
pp. 102440
Author(s):  
Sravanthi Alamandala ◽  
R.L.N. Sai Prasad ◽  
Rathish Kumar Pancharathi ◽  
V.D.R. Pavan ◽  
P. Kishore
Keyword(s):  
Strain Measurement ◽  
Weigh In Motion ◽  
Bridge Weigh In Motion ◽  
Fbg Sensors

Virtual Monitoring of orthotropic steel deck using bridge weigh-in-motion algorithm: Case study

2018 ◽  
Vol 18 (2) ◽  
pp. 610-620 ◽  
Author(s):  
Longwei Zhang ◽  
Hua Zhao ◽  
Eugene J OBrien ◽  
Xudong Shao
Keyword(s):  
Field Tests ◽  
Life Assessment ◽  
Dynamic Strain ◽  
Orthotropic Steel Deck ◽  
Remaining Service Life ◽  
Fatigue Life Assessment ◽  
Weigh In Motion ◽  
Gross Vehicle Weight ◽  
Bridge Weigh In Motion ◽  
Steel Deck

This article outlines a Virtual Monitoring approach for fatigue life assessment of orthotropic steel deck bridges. Bridge weigh-in-motion was used to calculate traffic loads which were then used to calculate “virtual” strains. Some of these strains were checked through long-term monitoring of dynamic strain data. Field tests, incorporating calibration with pre-weighed trucks and monitoring the response to regular traffic, were conducted at Fochen Bridge, which has an orthotropic steel deck and is located in Foshan City, China. In the calibration tests, a 45-t 3-axle truck ran repeatedly across Lane 2, the middle lane in a 3-lane carriageway. The results show that using an influence surface to weigh vehicles can improve the accuracy of the weights and, by inference, of remaining service life calculations. The most fatigue-prone position was found to be at the cutout in the diaphragms. Results show that many vehicles are overweight—the maximum gross vehicle weight recorded was 148 t, nearly 3.6 times heavier than the fatigue design truck.

scholarly journals Estudio numérico de la influencia del ruido presente en la señal, la ubicación de los sensores y la tasa de muestreo sobre el método BWIM basado en deformaciones unitarias

2017 ◽  
Vol 6 (1) ◽  
pp. 35-43
Author(s):  
Sergio Lobo Aguilar ◽  
Richard E. Christenson
Keyword(s):  
Weigh In Motion ◽  
Bridge Weigh In Motion

Los sistemas de pesaje en movimiento para puentes, en inglés Bridge-Weigh-In-Motion (BWIM), han sido utilizados con éxito para identificar algunas de las propiedades de los vehículos pesados que transitan sobre una vía. No obstante, las fuentes de incertidumbre no son siempre fáciles de controlar y por lo tanto podrían afectar la confiabilidad de los resultados. En esta investigación, se estudió la influencia de algunas de las dificultades experimentales que se presentan al utilizar el método BWIM basado en deformaciones unitarias. En particular, se analizaron tres efectos: el nivel de ruido presente en la señal, la ubicación de los sensores en el puente y la tasa de muestreo utilizada para la recolección de datos. Estos efectos se simularon numéricamente al añadir ruido generado sintéticamente a una onda de respuesta idealizada. En este trabajo se discuten los resultados con énfasis en posibles consecuencias sobre las estimaciones de velocidad y peso de vehículos pesados en pruebas de campo.

A gradient based optimization procedure for finding axle weights in probabilistic bridge weigh-in-motion method

2020 ◽  
Author(s):  
Matheus Silva Gonçalves ◽  
Felipe Carraro ◽  
Rafael Holdorf Lopez
Keyword(s):  
Computational Complexity ◽  
Optimization Procedure ◽  
Grid Search ◽  
Original Algorithm ◽  
Influence Line ◽  
Weigh In Motion ◽  
Gradient Based ◽  
Bridge Weigh In Motion ◽  
Proposed Modification

Bridge weight in motion (BWIM) consists in the use of sensors on bridges to assess the loads of passing vehicles. Probabilistic Bridge Weight in Motion (pBWIM) is an approach for solving the inverse problem of finding vehicle axle weights based on deformation information. The pBWIM approach uses a probabilistic influence line and seeks the most probable axle weights, given in-situ measurements. To compute such weights, the original pBWIM employed a grid search, which may lead to computational complexity, specially when applied to vehicles with high number of axles. Hence, this note presents an improved version of pBWIM, modifying how the most probable weights are sough. Here, a gradient based optimization procedure is proposed for replacing the computationally expensive grid-search of the original algorithm. The required gradients are fully derived and validated in numerical examples. The proposed modification is shown to highly decrease the computational complexity of the problem.

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