Bridge Influence Line Estimation for Bridge Weigh-in-Motion System

2015 ◽  
Vol 29 (1) ◽  
pp. 06014006 ◽  
Author(s):  
Sio-Song Ieng
Keyword(s):  
Motion System ◽  
Influence Line ◽  
Weigh In Motion ◽  
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.

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.

scholarly journals Testing of Bridge Weigh-In-Motion System in a Sub-Arctic Climate

2003 ◽  
Vol 31 (6) ◽  
pp. 11686 ◽  
Author(s):  
DR Petersen ◽  
RE Link ◽  
P McNulty ◽  
EJ O'Brien
Keyword(s):  
Arctic Climate ◽  
Motion System ◽  
Weigh In Motion ◽  
Bridge Weigh In Motion

scholarly journals A Simplified Shear-Strain Based Bridge Weigh-In-Motion Method For In-Service Highway Bridges

2019 ◽  
Vol 8 ◽  
pp. 11-22
Author(s):  
Sergio Lobo Aguilar ◽  
Richard E. Christenson
Keyword(s):  
Shear Strain ◽  
Field Tests ◽  
Highway Bridges ◽  
Influence Line ◽  
Bending Strain ◽  
Weigh In Motion ◽  
Gross Vehicle Weight ◽  
Bridge Weigh In Motion ◽  
The Difference ◽  
Motion Method

Bridge Weigh-In-Motion (BWIM) has been demonstrated to be reliable for obtaining critical information about the characteristics of trucks that travel over the highways. Continued improvements provides greater opportunity for increased use of BWIM. Traditional BWIM systems based on measuring the bending strain of the bridge have various challenges which has led to a class of BWIM methodologies that employ the use of shear strain in determining the gross vehicle weight (GVW) of crossing trucks. However, the known techniques of these shear-strain BWIM methods assume or measure the shear influence line for the calculation of the GVW. In this paper, an alternative shear-strain based BWIM technique is proposed. The method presented here is independent of the influence line, does not require a measurement of the speed of the truck, and is based on the difference in magnitude observed at the discontinuity of the shear strain record as a truck crosses over the sensor location on the bridge. A series of field tests is presented that demonstrate this shear-strain based BWIM method has error levels consistent with other more complex BWIM methods and as such has great potential to be used for determining the GVWs of trucks that travel on simple or multispan bridges in a consistent and reliable manner.

scholarly journals Direct measurement of dynamics in road bridges using a bridge weigh-in-motion system

2013 ◽  
Vol 8 (4) ◽  
pp. 263-270 ◽  
Author(s):  
Eugene J OBrien ◽  
Arturo González ◽  
Jason Dowling ◽  
Aleš Žnidarič
Keyword(s):  
Direct Measurement ◽  
Motion System ◽  
Weigh In Motion ◽  
Bridge Weigh In Motion

scholarly journals Probabilistic bridge weigh-in-motion

2018 ◽  
Vol 45 (8) ◽  
pp. 667-675 ◽  
Author(s):  
Eugene J. OBrien ◽  
Longwei Zhang ◽  
Hua Zhao ◽  
Donya Hajializadeh
Keyword(s):  
Field Tests ◽  
Bending Moment ◽  
Vehicle Speed ◽  
Strain Response ◽  
Simply Supported ◽  
Influence Line ◽  
Weigh In Motion ◽  
Alternative Approach ◽  
Bridge Weigh In Motion ◽  
Good Potential

Conventional bridge weigh-in-motion (BWIM) uses a bridge influence line to find the axle weights of passing vehicles that minimize the sum of squares of differences between theoretical and measured responses. An alternative approach, probabilistic bridge weigh-in-motion (pBWIM), is proposed here. The pBWIM approach uses a probabilistic influence line and seeks to find the most probable axle weights, given the measurements. The inferred axle weights are those with the greatest probability amongst all possible combinations of values. The measurement sensors used in pBWIM are similar to BWIM, containing free-of-axle detector sensors to calculate axle spacings and vehicle speed and weighing sensors to record deformations of the bridge. The pBWIM concept is tested here using a numerical model and a bridge in Slovenia. In a simulation, 200 randomly generated 2-axle trucks pass over a 6 m long simply supported beam. The bending moment at mid-span is used to find the axle weights. In the field tests, 77 pre-weighed trucks traveled over an integral slab bridge and the strain response in the soffit at mid-span was recorded. Results show that pBWIM has good potential to improve the accuracy of BWIM.

scholarly journals Development and Testing of a Railway Bridge Weigh-in-Motion System

2020 ◽  
Vol 10 (14) ◽  
pp. 4708 ◽  
Author(s):  
Donya Hajializadeh ◽  
Aleš Žnidarič ◽  
Jan Kalin ◽  
Eugene John OBrien
Keyword(s):  
Constant Velocity ◽  
Regression Models ◽  
Variable Velocity ◽  
Railway Bridge ◽  
Motion System ◽  
Weigh In Motion ◽  
Calibration Methods ◽  
Bridge Weigh In Motion ◽  
Bridge Wim ◽  
Measured Response

This study describes the development and testing of a railway bridge weigh-in-motion (RB-WIM) system. The traditional bridge WIM (B-WIM) system developed for road bridges was extended here to calculate the weights of railway carriages. The system was tested using the measured response from a test bridge in Poland, and the accuracy of the system was assessed using statically-weighed trains. To accommodate variable velocity of the trains, the standard B-WIM algorithm, which assumes a constant velocity during the passage of a vehicle, was adjusted and the algorithm revised accordingly. The results showed that the vast majority of the calculated carriage weights fell within ±5% of their true, statically-weighed values. The sensitivity of the method to the calibration methods was then assessed using regression models, trained by different combinations of calibration trains.

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