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 Monitoring of Changes in Bridge Response Using Weigh-In-Motion Systems

2013 ◽  
Vol 569-570 ◽  
pp. 183-190
Author(s):  
Daniel Cantero ◽  
Arturo González ◽  
Biswajit Basu
Keyword(s):  
Structural Damage ◽  
Element Model ◽  
Traffic Loading ◽  
Damage Scenarios ◽  
The Road ◽  
The Past ◽  
Road Pavement ◽  
Damage Indicator ◽  
Weigh In Motion ◽  
Bridge Weigh In Motion

Weigh-In-Motion (WIM) and Bridge Weigh-In-Motion (B-WIM) are systems that allow obtaining the axle weights of road vehicles in motion, at normal traffic speeds. While WIM employs sensors embedded in the road pavement, B-WIM use the strain recordings of a bridge to infer the traversing vehicle axle weights. Both systems have been heavily improved over the past decades, and commercial versions are currently in operation. The two main applications of these systems are: (1) to assess the traffic loading on the infrastructure, and (2) to enforce the maximum weight limits. This paper suggests a novel application of these two systems to identify changes in bridge stiffness. It requires the bridge to be instrumented with a B-WIM system and a WIM system nearby. The principle is to use both systems to evaluate the gross weight of vehicles passing over the bridge and correlate their predictions. Changes in correlation of the predicted axle weights over time will indicate either structural damage or faulty sensor. A finite element model of a coupled vehicle-bridge system with different damage scenarios is used to test the approach numerically. Vehicle mechanical properties and speeds are randomly sampled within a Monte Carlo simulation. Results show how correlation changes as damage increases and how this correlation can be employed as a damage indicator.

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 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.

Sparse damage detection via the elastic net method using modal data

2021 ◽  
pp. 147592172110219
Author(s):  
Rongrong Hou ◽  
Xiaoyou Wang ◽  
Yong Xia
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Damage Identification ◽  
Measurement Data ◽  
Elastic Net ◽  
Sensitivity Matrix ◽  
Regularization Technique ◽  
Modal Data ◽  
Regularization Techniques ◽  
Net Method

The l1 regularization technique has been developed for damage detection by utilizing the sparsity feature of structural damage. However, the sensitivity matrix in the damage identification exhibits a strong correlation structure, which does not suffice the independency criteria of the l1 regularization technique. This study employs the elastic net method to solve the problem by combining the l1 and l2 regularization techniques. Moreover, the proposed method enables the grouped structural damage being identified simultaneously, whereas the l1 regularization cannot. A numerical cantilever beam and an experimental three-story frame are utilized to demonstrate the effectiveness of the proposed method. The results showed that the proposed method is able to accurately locate and quantify the single and multiple damages, even when the number of measurement data is much less than the number of elements. In particular, the present elastic net technique can detect the grouped damaged elements accurately, whilst the l1 regularization method cannot.

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.

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