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.

scholarly journals Using Statistical Analysis of an Acceleration-Based Bridge Weigh-In-Motion System for Damage Detection

2020 ◽  
Vol 10 (2) ◽  
pp. 663 ◽  
Author(s):  
Eugene OBrien ◽  
Muhammad Arslan Khan ◽  
Daniel Patrick McCrum ◽  
Aleš Žnidarič
Keyword(s):  
Statistical Analysis ◽  
Damage Detection ◽  
Interaction Model ◽  
Statistical Properties ◽  
Vehicle Weight ◽  
Motion System ◽  
Weigh In Motion ◽  
Gross Vehicle Weight ◽  
Bridge Damage ◽  
Bridge Weigh In Motion

This paper develops a novel method of bridge damage detection using statistical analysis of data from an acceleration-based bridge weigh-in-motion (BWIM) system. Bridge dynamic analysis using a vehicle-bridge interaction model is carried out to obtain bridge accelerations, and the BWIM concept is applied to infer the vehicle axle weights. A large volume of traffic data tends to remain consistent (e.g., most frequent gross vehicle weight (GVW) of 3-axle trucks); therefore, the statistical properties of inferred vehicle weights are used to develop a bridge damage detection technique. Global change of bridge stiffness due to a change in the elastic modulus of concrete is used as a proxy of bridge damage. This approach has the advantage of overcoming the variability in acceleration signals due to the wide variety of source excitations/vehicles—data from a large number of different vehicles can be easily combined in the form of inferred vehicle weight. One year of experimental data from a short-span reinforced concrete bridge in Slovenia is used to assess the effectiveness of the new approach. Although the acceleration-based BWIM system is inaccurate for finding vehicle axle-weights, it is found to be effective in detecting damage using statistical analysis. It is shown through simulation as well as by experimental analysis that a significant change in the statistical properties of the inferred BWIM data results from changes in the bridge condition.

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.

Proposed Standard Fatigue Truck in Montane Speedway of Southwest China

2011 ◽  
Vol 90-93 ◽  
pp. 1239-1244
Author(s):  
Ji Wei Zhong ◽  
Kun Quan Huang ◽  
Xing Xin Li
Keyword(s):  
Effective Stress ◽  
Southwest China ◽  
Peak Stress ◽  
Traffic Data ◽  
Stress Range ◽  
Weigh In Motion ◽  
Bridge Model ◽  
Actual Damage ◽  
The Moment ◽  
Fatigue Evaluation

The fatigue truck model is an important parameter in a fatigue evaluation, a 3-axle fatigue truck model was developed based on the weigh-in-motion traffic data and an analytical bridge model in the montane highway of Southwest China. The truck traffic data shows that the fatigue damage was dominated by the 6-axles trucks,a 3-axle fatigue truck model was developed based on the 6-axle truck statistic data,the damage accumulations caused by the Proposed fatigue truck righty meet the actual damage accumulations. Based on the cumulative probabilitie of the moment ranges, the peak stress range is suggested to be a stress level at 3 times of the effective stress range because of overload,however,the ratio of the effective stress in AASHTO was 2 times.The damage accumulations obtained from the simulation of the truck database were compared with BS5400,AASHTO and Proposed fatigue truck, Proposed fatigue truck and AASHTO fatigue truck with the actural daily flow of trucks are suggested in the montane speedway of Southwest China ,BS5400 is impropriety which relatively overestimate the damage.

scholarly journals Bridge Damage Detection Using Weigh-in-Motion Technology

2015 ◽  
Vol 20 (5) ◽  
pp. 04014078 ◽  
Author(s):  
Daniel Cantero ◽  
Arturo González
Keyword(s):  
Damage Detection ◽  
Weigh In Motion ◽  
Bridge Damage

scholarly journals Simultaneous Incomplete Traffic Data Imputation and Similarity Pattern Discovery with Bayesian Nonparametric Tensor Decomposition

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yaxiong Han ◽  
Zhaocheng He
Keyword(s):  
Dirichlet Process ◽  
Traffic Management ◽  
Pattern Discovery ◽  
Tensor Decomposition ◽  
Data Imputation ◽  
Traffic Data ◽  
Bayesian Nonparametric ◽  
Dirichlet Process Mixture ◽  
Wide Range ◽  
Similarity Pattern

A crucial task in traffic data analysis is similarity pattern discovery, which is of great importance to urban mobility understanding and traffic management. Recently, a wide range of methods for similarities discovery have been proposed and the basic assumption of them is that traffic data is complete. However, missing data problem is inevitable in traffic data collection process due to a variety of reasons. In this paper, we propose the Bayesian nonparametric tensor decomposition (BNPTD) to achieve incomplete traffic data imputation and similarity pattern discovery simultaneously. BNPTD is a hierarchical probabilistic model, which is comprised of Bayesian tensor decomposition and Dirichlet process mixture model. Furthermore, we develop an efficient variational inference algorithm to learn the model. Extensive experiments were conducted on a smart card dataset collected in Guangzhou, China, demonstrating the effectiveness of our methods. It should be noted that the proposed BNPTD is universal and can also be applied to other spatiotemporal traffic data.

scholarly journals A direct method to detect and localise damage using longitudinal data of ends-of-span rotations under live traffic loading

2021 ◽  
Author(s):  
Alan J. Ferguson ◽  
David Hester ◽  
Roger Woods
Keyword(s):  
Damage Detection ◽  
Direct Method ◽  
Measurement Data ◽  
Cost Effective ◽  
Free Flow ◽  
Traffic Loading ◽  
Rotation Measurement ◽  
Weigh In Motion ◽  
Direct Rotation ◽  
Bridge Weigh In Motion

AbstractExisting work on rotation-based bridge monitoring has focused on indirect methods, such as bridge weigh-in-motion or influence line approaches. However, these approaches require increased instrumentation complexity, and require calibration, necessitating bridge closures. In this paper, we explore the potential of using rotation measurements to create a more practical and cost-effective monitoring system. To this end, we present a damage detection method which directly analyses bridge rotation data measured under live, free-flow traffic loading. We show how the Earth Mover’s Distance, typically used in statistics and image processing, can be applied directly on end-of-span rotation measurement data to achieve effective damage detection and localisation. Numerical simulation results demonstrate the approach’s robustness to the confounding effects of temperature variation and traffic diversity (vehicle type, loading, and velocity). The direct rotation measurement approach is applied to data from an in-service short-span bridge to demonstrate the technique’s capability with free-flow traffic loading.

scholarly journals Bridge Damage Detection Approach Using a Roving Camera Technique

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1246 ◽  
Author(s):  
Darragh Lydon ◽  
Myra Lydon ◽  
Rolands Kromanis ◽  
Chuan-Zhi Dong ◽  
Necati Catbas ◽  
...  
Keyword(s):  
Low Cost ◽  
Normal Operation ◽  
Laboratory Model ◽  
Traffic Patterns ◽  
Operational Conditions ◽  
Bridge Model ◽  
Detection Approach ◽  
Bridge Damage ◽  
Computer Vision Technology ◽  
Climate Events

Increasing extreme climate events, intensifying traffic patterns and long-term underinvestment have led to the escalated deterioration of bridges within our road and rail transport networks. Structural Health Monitoring (SHM) systems provide a means of objectively capturing and quantifying deterioration under operational conditions. Computer vision technology has gained considerable attention in the field of SHM due to its ability to obtain displacement data using non-contact methods at long distances. Additionally, it provides a low cost, rapid instrumentation solution with low interference to the normal operation of structures. However, even in the case of a medium span bridge, the need for many cameras to capture the global response can be cost-prohibitive. This research proposes a roving camera technique to capture a complete derivation of the response of a laboratory model bridge under live loading, in order to identify bridge damage. Displacement is identified as a suitable damage indicator, and two methods are used to assess the magnitude of the change in global displacement under changing boundary conditions in the laboratory bridge model. From this study, it is established that either approach could detect damage in the simulation model, providing an SHM solution that negates the requirement for complex sensor installations.

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