The Virtual Axle concept for detection of localised damage using Bridge Weigh-in-Motion data

Cantero, Daniel; Karoumi, Raid; González, Arturo

doi:10.1016/j.engstruct.2015.02.001

Cited by 25 publications

(24 citation statements)

References 9 publications

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“…The proposed system was able to determine the volume of trucks crossing the bridge, their gross vehicle weights, the lanes used by the trucks, and the number of overload trucks. Cantero et al [139] proposed a BWIM-based damage identification method by introducing the concept of 'Virtual Axle' to derive a damage indicator. The investigations on the influence of the key parameters such as the degree and location of damage, noise levels, span lengths, and profile irregularities on the accuracy of the method show that the 'Virtual Axle' method can detect small local damages in statically indeterminate structures.…”

Section: Vehicle-classification-based Methodsmentioning

confidence: 99%

“…Kalyankar and Uddin [141] BWIM Vehicle characteristic Obtain vehicle parameters such as velocity, axle numbers, and their distances Fischli et al [157] Fiber-optic strain gauges (FBG) -Number of axes per vehicle and driving speed Fischli et al [157] Long-gauge strain influence line The influence line of long-gauge strain Axle load, wheelbase and velocity on a bridge Gonzalez and Karoumi [140] BWIM Load's position, magnitude and speed Assessing healthy or damaged state of bridge Cantero et al [139] BWIM Bridge deformation Detect small local damages Cantero and González [134] WIM Deformation of the bridge Axle weights and distances between axles for each vehicle Health monitoring of bridges using traffic information obtained using vehicle classification methods such as WIM, BWIM or strain gauges has been practiced for many years, and methods and apparatus used prior, have been modified to suit the then-current needs. However, the concept and technical principles of these methods remained largely unchanged for more than a half-century whereas the vehicles are undergoing a distinct evolution in design, and technology.…”

Section: Vehicle-classification-based Methodsmentioning

confidence: 99%

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Vehicle-Assisted Techniques for Health Monitoring of Bridges

Shokravi

Bakhary

et al. 2020

Sensors

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Bridges are designed to withstand different types of loads, including dead, live, environmental, and occasional loads during their service period. Moving vehicles are the main source of the applied live load on bridges. The applied load to highway bridges depends on several traffic parameters such as weight of vehicles, axle load, configuration of axles, position of vehicles on the bridge, number of vehicles, direction, and vehicle’s speed. The estimation of traffic loadings on bridges are generally notional and, consequently, can be excessively conservative. Hence, accurate prediction of the in-service performance of a bridge structure is very desirable and great savings can be achieved through the accurate assessment of the applied traffic load in existing bridges. In this paper, a review is conducted on conventional vehicle-based health monitoring methods used for bridges. Vision-based, weigh in motion (WIM), bridge weigh in motion (BWIM), drive-by and vehicle bridge interaction (VBI)-based models are the methods that are generally used in the structural health monitoring (SHM) of bridges. The performance of vehicle-assisted methods is studied and suggestions for future work in this area are addressed, including alleviating the downsides of each approach to disentangle the complexities, and adopting intelligent and autonomous vehicle-assisted methods for health monitoring of bridges.

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Section: Vehicle-classification-based Methodsmentioning

confidence: 99%

Section: Vehicle-classification-based Methodsmentioning

confidence: 99%

Vehicle-Assisted Techniques for Health Monitoring of Bridges

Shokravi

Bakhary

et al. 2020

Sensors

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“…In this study, a damage indicator is defined as the relative difference in gross vehicle weight inferred by the two systems. In [19] the authors introduce a fictitious weightless axle, termed a Virtual Axle, in the Weigh-in-Motion algorithm to derive a damage indicator. In essence, if damage occurs then the B-WIM algorithm overestimates the weight of the virtual axle.…”

Section: Introductionmentioning

confidence: 99%

“…To simulate the presence of a diaphragm at both ends of the bridge, the second moment of area of the beams is set higher for elements close to the supports in comparison to the rest of elements. As a result, the 2nd moment of area of the beam elements between x = 1 m and x = 19 m (I b ) equalled 0.0685 m 4 while in the ranges x = [0,1] and[19,20] m (I b,supports ) it was increased by up to 0.108 m 4 .…”

mentioning

confidence: 96%

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

O’Brien

Hester

Huseynov

et al. 2020

J Civil Struct Health Monit

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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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“…Chen et al (2015) proposed a regularization method to identify the stress influence lines (SILs) based on the on-site measurement of train information and traininduced stress response in local bridge components, and it was found that the first-order difference of SIL change was an accurate indicator of the damage location. Cantero and Gonzalez (2015) presented a bridge damage detection technique for short-to-medium-span bridges using weigh-in-motion (WIM) technology. The technique is based on a hybrid WIM system that includes a pavement-based WIM station and a bridgebased WIM system.…”

Section: Bridge Condition Assessment Based Onmentioning

confidence: 99%

Structural Health Monitoring Based on Vehicle-Bridge Interaction: Accomplishments and Challenges

Zhu

Law

2015

Advances in Structural Engineering

134

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The use of the vehicle-bridge interaction (VBI) data for structural health monitoring has received considerable interest in the last decade. Compared with the traditional bridge health monitoring, the VBI based approach allows the target bridges to be monitored or assessed under operating conditions. The VBI system has time-variant features and the vehicle can serve as a moving exciter and a mobile sensor in the system. Many bridge damage identification techniques based on VBI have been developed, and they could be divided into three categories, namely, technique based on the bridge responses, technique based on the vehicle responses and technique based on both the vehicle and bridge responses. This paper presents a review on the structural health monitoring based on VBI and the challenges for its general implementation in practice.

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The Virtual Axle concept for detection of localised damage using Bridge Weigh-in-Motion data

Cited by 25 publications

References 9 publications

Vehicle-Assisted Techniques for Health Monitoring of Bridges

Vehicle-Assisted Techniques for Health Monitoring of Bridges

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

Structural Health Monitoring Based on Vehicle-Bridge Interaction: Accomplishments and Challenges

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