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 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.

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

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>

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.

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.

Field Verification of Moving Load Model for Pavement Response

1996 ◽  
Vol 1540 (1) ◽  
pp. 125-131 ◽  
Author(s):  
Raj Siddharthan ◽  
Jian Yao ◽  
Peter E. Sebaaly
Keyword(s):  
Moving Load ◽  
Field Tests ◽  
Field Testing ◽  
Material Behavior ◽  
Vehicle Speed ◽  
Strain Response ◽  
State University ◽  
Load Model ◽  
Pavement Response ◽  
The Difference

A validation study undertaken to verify the predictive capability of a recently developed moving load model to predict pavement response is described. The full-scale field-measured responses of longitudinal strain at the bottom of the asphalt concrete (AC) layer were used in the verification. The field testing program, in which the strain responses induced by a semitrailer truck were measured as a function of vehicle speed, was carried out at the Pennsylvania State University test track. The material behavior of the AC layer, which was assumed to be viscoelastic, was deduced from the laboratory behavior of the AC and from the backcalculated AC modulus from falling-weight deflectometer data. The unbound material layer properties were assumed to be elastic. The moving load model reproduced many important general observations made from the field tests, such as the existence of a complex interaction in the case of a tandem axle configuration and the strong influence of vehicle speed on the strain response. Good agreement exists between the predictions made by the model for the strain response for single and tandem axle configurations under different loading and vehicle speeds and those measured in the field. The difference is less than 14 percent, thus verifying the applicability of the moving load model to predict pavement response.

scholarly journals Wavelet-Based Optimum Identification of Vehicle Axles Using Bridge Measurements

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.

scholarly journals Identifying damage on a bridge using rotation-based Bridge Weigh-In-Motion

2020 ◽  
Author(s):  
E. J. OBrien ◽  
J. M. W. Brownjohn ◽  
D. Hester ◽  
F. Huseynov ◽  
M. Casero
Keyword(s):  
Damage Detection ◽  
Traffic Management ◽  
Traffic Data ◽  
Simply Supported ◽  
Weigh In Motion ◽  
Bridge Model ◽  
Wide Range ◽  
Bridge Condition Assessment ◽  
Bridge Damage ◽  
Bridge Weigh In Motion

Abstract Bridge Weigh-in-Motion (B-WIM) systems use the bridge response under a traversing vehicle to estimate its axle weights. The information obtained from B-WIM systems has been used for a wide range of applications such as pre-selection for weight enforcement, traffic management/planning and for bridge and pavement design. However, it is less often used for bridge condition assessment purposes which is the main focus of this study. This paper presents a bridge damage detection concept using information provided by B-WIM systems. However, conventional B-WIM systems use strain measurements which are not sensitive to local damage. In this paper the authors present a B-WIM formulation that uses rotation measurements obtained at the bridge supports. There is a linear relationship between support rotation and axle weight and, unlike strain, rotation is sensitive to damage anywhere in the bridge. Initially, the sensitivity of rotation to damage is investigated using a hypothetical simply supported bridge model. Having seen that rotation is damage-sensitive, the influence of bridge damage on weight predictions is analysed. It is shown that if damage occurs, a rotation-based B-WIM system will continuously overestimate the weight of traversing vehicles. Finally, the statistical repeatability of ambient traffic is studied using real traffic data obtained from a Weigh-in-Motion site in the U.S. under the Federal Highway Administration’s Long-Term Pavement Performance programme and a damage indicator is proposed as the change in the mean weights of ambient traffic data. To test the robustness of the proposed damage detection methodology numerical analysis are carried out on a simply supported bridge model and results are presented within the scope of this study.

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