Development and testing of a bridge weigh-in-motion method considering nonconstant vehicle speed

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.

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Probabilistic bridge weigh-in-motion

Canadian Journal of Civil Engineering ◽

10.1139/cjce-2017-0508 ◽

2018 ◽

Vol 45 (8) ◽

pp. 667-675 ◽

Cited By ~ 9

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.

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Wavelet-Based Optimum Identification of Vehicle Axles Using Bridge Measurements

Applied Sciences ◽

10.3390/app10217485 ◽

2020 ◽

Vol 10 (21) ◽

pp. 7485

Author(s):

Hua Zhao ◽

Chengjun Tan ◽

Eugene J. OBrien ◽

Nasim Uddin ◽

Bin Zhang

Keyword(s):

Field Trials ◽

Correlation Factor ◽

Vehicle Speed ◽

The Road ◽

Bridge Health Monitoring ◽

Axle Detection ◽

Weigh In Motion ◽

Bridge Weigh In Motion ◽

Health Monitoring Systems ◽

On The Road

Accurate vehicle configurations (vehicle speed, number of axles, and axle spacing) are commonly required in bridge health monitoring systems and are prerequisites in bridge weigh-in-motion (BWIM) systems. Using the ‘nothing on the road’ principle, this data is found using axle detecting sensors, usually strain gauges, placed at particular locations on the underside of the bridge. To improve axle detection in the measured signals, this paper proposes a wavelet transform and Shannon entropy with a correlation factor. The proposed approach is first verified by numerical simulation and is then tested in two field trials. The fidelity of the proposed approach is investigated including noise in the measurement, multiple presence, different vehicle velocities, different types of vehicle and in real traffic flow.

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Effectiveness of Vehicle Weight Estimation from Bridge Weigh-in-Motion

Advances in Civil Engineering ◽

10.1155/2009/312034 ◽

2009 ◽

Vol 2009 ◽

pp. 1-13 ◽

Cited By ~ 5

Author(s):

Teerachai Deesomsuk ◽

Tospol Pinkaew

Keyword(s):

Surface Roughness ◽

Estimation Methods ◽

Estimation Accuracy ◽

Vehicle Speed ◽

Time Varying ◽

Weight Estimation ◽

Vehicle Weight ◽

Weigh In Motion ◽

Bridge Weigh In Motion ◽

Axle Loads

The effectiveness of vehicle weight estimations from bridge weigh-in-motion system is studied. The measured bending moments of the instrumented bridge under a passage of vehicle are numerically simulated and are used as the input for the vehicle weight estimations. Two weight estimation methods assuming constant magnitudes and time-varying magnitudes of vehicle axle loads are investigated. The appropriate number of bridge elements and sampling frequency are considered. The effectiveness in term of the estimation accuracy is evaluated and compared under various parameters of vehicle-bridge system. The effects of vehicle speed, vehicle configuration, vehicle weight and bridge surface roughness on the accuracy of the estimated vehicle weights are intensively investigated. Based on the obtained results, vehicle speed, surface roughness level and measurement error seem to have stronger effects on the weight estimation accuracy than other parameters. In general, both methods can provide quite accurate weight estimation of the vehicle. Comparing between them, although the weight estimation method assuming constant magnitudes of axle loads is faster, the method assuming time-varying magnitudes of axle loads can provide axle load histories and exhibits more accurate weight estimations of the vehicle for almost of the considered cases.

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A bridge weigh-in-motion method of motorway bridges considering random traffic flow based on long-gauge fibre Bragg grating sensors

Measurement ◽

10.1016/j.measurement.2021.110081 ◽

2021 ◽

pp. 110081

Author(s):

Lu Zhang ◽

Xiaoxiang Cheng ◽

Gang Wu

Keyword(s):

Traffic Flow ◽

Bragg Grating ◽

Fibre Bragg Grating ◽

Weigh In Motion ◽

Bridge Weigh In Motion ◽

Motion Method

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Calculation of influence line using regular traffic for improving bridge weigh-in-motion under variable vehicle speed

IABSE Congress, Christchurch 2021: Resilient technologies for sustainable infrastructure ◽

10.2749/christchurch.2021.0366 ◽

2021 ◽

Author(s):

Kouichi Takeya ◽

Junji Yoshida ◽

Junki Mori

Keyword(s):

Flow Analysis ◽

Iteration Process ◽

Video Camera ◽

Gaussian Mixture ◽

Baseline Correction ◽

Vehicle Speed ◽

Integration Process ◽

Influence Line ◽

Weigh In Motion ◽

Bridge Weigh In Motion

<p>In this study, an influence line of girder deflection of the bridge was calculated for the initial calibration of Bridge Weigh-in-Motion (B-WIM). The deflection responses were obtained from the proposed integration process using the baseline correction. Optical flow analysis was applied using a video camera to adapt to the variable vehicle speed and precisely measure the location of vehicles on a bridge. A foreground mask using the Gaussian mixture model and a Kalman filter was then applied to identify the vehicles. A calibration process of B-WIM was proposed using the iteration process to optimize the influence line of deflection using local buses in regular traffic. Finally, the axle weights of a weight-known test truck were analyzed by monitoring with the video camera and acceleration sensor. Compared with conventional B-WIM methods, the proposed method has demonstrated higher adaptability in variable vehicle speed.</p>

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The identification technology of vehicle weight based on bridge strain time-history curve

Advances in Structural Engineering ◽

10.1177/1369433218823487 ◽

2019 ◽

Vol 22 (7) ◽

pp. 1606-1616

Author(s):

Tianzhi Hao ◽

Zhengyuan Xie ◽

Mengsheng Yu

Keyword(s):

Time History ◽

Experimental Result ◽

Vehicle Speed ◽

Continuous Beam ◽

Practical Application ◽

Vehicle Weight ◽

Weigh In Motion ◽

Bridge Weigh In Motion ◽

Practical Model ◽

Basic Characteristics

It is the basic characteristics of bridge weigh-in-motion technology to directly identify the vehicle weight based on the bridge dynamic response. At present, bridge weigh-in-motion technology tends to be mature in identification of gross vehicle weighing, but there is no breakthrough in identification of single-axle weighing. Therefore, a new axle-weight identification method is proposed using bridge weigh-in-motion technology in this article, in which the idea of bridge weigh-in-motion technology is introduced first. The numerical expression of the single-axle weight and the identification expression of axle space and vehicle speed are presented thereafter. Furthermore, the accuracy of the presented method is further reinforced through a series of practical model experiments of simply supported and continuous beam. The experimental result indicated that the proposed method is feasible in practical application.

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Comparative Accuracy Analysis of Truck Weight Measurement Techniques

Applied Sciences ◽

10.3390/app11020745 ◽

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.

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