Wavelet domain analysis for identification of vehicle axles from bridge measurements

Chatterjee, Prasenjit; O’Brien, Eugene J.; Li, Yingyan; González, Arturo

doi:10.1016/j.compstruc.2006.04.013

Cited by 70 publications

(46 citation statements)

References 15 publications

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“…However, this method of axle detection is largely dependent on the transverse position of the wheels -the slab bends when the wheel passes along it but bends much less so when the wheel falls runs directly over the beam. This is widely accepted in the literature (Chatterjee et al, 2006;Kalin et al, 2006) and was also encountered at the Loughbrickland site. Figure 7 shows a partly loaded (22 t) class 52 vehicle where neither the axles of the tandem nor the individual axles of the tractor are distinguishable.…”

Section: Bwim Resultssupporting

confidence: 65%

Bridge weigh-in-motion using fibre optic sensors

Lydon¹,

Taylor²,

Doherty³

et al. 2017

Proceedings of the Institution of Civil Engineers - Bridge Engi

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Bridge weigh-in-motion systems were introduced in the 1970s as a structural health monitoring tool for road bridges. They help bridge operators determine the cause of induced strain in bridges and collect statistics on vehicle weight, class and frequency. There have been over 3000 installations in 25 countries worldwide, which has led to vast improvements in data post-processing. However, existing systems are based on electrical resistance strain gauges, which can be prohibitive in achieving data for long-term monitoring of rural bridges due to power consumption. This paper introduces a new low-power system using fibre optic sensors, which has been pioneered in Northern Ireland. A series of fibre optic sensors were attached to the soffit of an existing integral bridge with a single span of 19 m. The site selection criteria and full installation process are described in this paper. A method of calibration was adopted using live traffic at the bridge site, based on which the accuracy of the system was determined. New methods of axle detection for bridge weigh-in-motion were investigated and verified in the field.

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Section: Bwim Resultssupporting

confidence: 65%

Bridge weigh-in-motion using fibre optic sensors

Lydon¹,

Taylor²,

Doherty³

et al. 2017

Proceedings of the Institution of Civil Engineers - Bridge Engi

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show abstract

“…40), the frequency range in which all the relevant frequency components of the analyzed signal are contained are defined. This frequency interval according to (55) determines the size of the scale vector for wavelet coefficients matrix: According to (50), the wavelet transform is performed and matrix of wavelet coefficients is calculated. The scale gram showing wavelet coefficients matrix obtained in this operation is illustrated in Fig.…”

Section: Discussionmentioning

confidence: 99%

“…Since wavelet transform is a part of the multiresolution analysis, it gives a full insight into the time frequency character of a signal. For this reason, this approach is widely utilized in diagnostics of structures, constructions, and devices [46][47][48][49][50]. Wavelet-based methods also find applications, e.g., in control algorithms for autonomous vehicles [51], pattern recognition and texture classification [52], or automatic incident detection on freeways which is an important component of an Advanced Traffic Management and Information Systems [53].…”

Section: Introductionmentioning

confidence: 99%

Investigation of vehicle crash modeling techniques: theory and application

Pawlus

Karimi

Robbersmyr

2013

Int J Adv Manuf Technol

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Creating a mathematical model of a vehicle crash is a task which involves considerations and analysis of different areas which need to be addressed because of the mathematical complexity of a crash event representation. Therefore, to simplify the analysis and enhance the modeling process, in this work, a brief overview of different vehicle crash modeling methodologies is proposed. The acceleration of a colliding vehicle is measured in its center of gravity-this crash pulse contains detailed information about vehicle behavior throughout a collision. A virtual model of a collision scenario is established in order to provide an additional data set further used to evaluate a suggested approach. Three different approaches are discussed here: lumped parameter modeling of viscoelastic systems, data-based approach taking advantage of neural networks and autoregressive models and wavelet-based method of signal reconstruction. The comparative analysis between each method's outcomes is performed and reliability of the proposed methodologies and tools is evaluated.

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“…Velocity can be included in the optimization process (Dempsey et al 1998a;Žnidarič et al 1999a), a strategy which improves accuracy but which requires a good initial estimate. The identification of discontinuities in strain histories as axles pass overhead can be improved using wavelet domain analysis (Chatterjee et al 2006). More accurate measures of velocity result from this improved information on axle position at given points in time.…”

Section: Axle Detectionmentioning

confidence: 99%

On the use of bridge weigh-in-motion for overweight truck enforcement

Richardson

Jones

Brown

et al. 2014

IJHVS

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Bridge weigh-in-motion (B-WIM) is a method by which the axle weights of a vehicle travelling at full highway speed can be determined using a bridge instrumented with sensors. Since the sensors are attached to the underside of a bridge, the instrumentation can be installed without disruption to traffic. This paper looks at the history of B-WIM, beginning with early work on weigh-in-motion technologies in the 1960's leading to its invention by Fred Moses and George Goble in the United States in the mid 1970's. Particular attention is devoted to Moses' original algorithm, which has been used by many systems since 1979 and is still utilized today by commercial developers of B-WIM systems. Research initiatives in Australia and Europe over the past 15 years have focused on improving B-WIM accuracy either by improving Moses' original algorithm or by developing new methods. The moving force identification (MFI) method models the dynamic fluctuation of axle forces on the bridge and holds particular promise. B-WIM accuracy depends on bridge site conditions as well as the particular data processing algorithm. The accuracy classifications of several B-WIM installations reported in the literature are summarized in this paper. Current accuracy levels are sufficient for selecting vehicles to be weighed using static scales, but insufficient for direct enforcement.

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Wavelet domain analysis for identification of vehicle axles from bridge measurements

Cited by 70 publications

References 15 publications

Bridge weigh-in-motion using fibre optic sensors

Bridge weigh-in-motion using fibre optic sensors

Investigation of vehicle crash modeling techniques: theory and application

On the use of bridge weigh-in-motion for overweight truck enforcement

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