Moving Force Identification: Optimal State Estimation Approach

Law, S.S.; Fang, Yue

doi:10.1006/jsvi.2000.3118

Cited by 105 publications

(53 citation statements)

References 19 publications

(18 reference statements)

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“…In the late 1990s and early 2000s, several researchers, investigated the differences between theoretical B-WIM algorithms and bridge measurements, both theoretically using complex vehicle-bridge dynamic interaction models and experimentally using data from four bridge sites (González 2010;González and OBrien 1998;Law et al 1997;Law et al 1999;Yu and Chan 2003;Zhu and Law 2001;Law et al 2004;Law and Fang 2001). The influence of dynamics and multiple sensors on the accuracy of B-WIM systems is addressed.…”

Section: Dynamic Models and Moving Force Identificationmentioning

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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Section: Dynamic Models and Moving Force Identificationmentioning

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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“…Previous research [5] has developed theoretical models for B-WIM and demonstrated that Tikhonov Regularization can be used to improve ill-conditioned Moses equations which occur when axles are closely spaced relative to the bridge span. More recently, moving force identification (MFI) techniques have been applied to measured signals to improve the accuracy of the measured axle weights [6,9,10]. These techniques have been found to improve the accuracy of the systems [5].…”

Section: Introductionmentioning

confidence: 99%

Improved axle detection for bridge weigh-in-motion systems using fiber optic sensors

Lydon

Robinson

Taylor

et al. 2017

J Civil Struct Health Monit

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Bridge weigh-in-motion (B-WIM) systems provide a non-destructive means of gathering traffic loading information by using an existing bridge as a weighing scale to determine the weights of vehicles passing over. In this research critical locations for sensors for the next-generation B-WIM were determined from a full 3D explicit finite element analysis (FEA) model. Although fiber optic sensors (FOS) have become increasingly popular in SHM systems there are currently no commercially available fiber optic WIM systems available. The FEA in this research facilitated the development of the first ever full fiber optic B-WIM and its potential has been demonstrated with the site installation of this system. The system combined nothing-on-the-road axle detection and alternative methods of measuring strain at the supports. The system was installed on a 20-m span beam and slab RC bridge in Northern Ireland and the results presented in this paper confirm the suitability of FOS in providing the clear defined peaks required for accurate axle detection in B-WIM.

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“…First order Tikhonov regularisation is used to decrease errors due to illconditioning and the recursive least-squares problem is solved using the Dynamic Programming technique (Trujillo 1978) which has been utilised previously in force identification problems (Law and Fang 2001, Nordström 2006, González et al 2008b. A coupled vehicle-bridge interaction (VBI) model is created in MATLAB (2005) to simulate 'measured' accelerations.…”

Section: Introductionmentioning

confidence: 99%

A drive-by inspection system via vehicle moving force identification

O’Brien

McGetrick²,

González

2014

Smart Structures and Systems

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Publication informationSmart Structures and Systems, 13 (5) Abstract. This paper presents a novel method to carry out monitoring of transport infrastructure such as pavements and bridges through the analysis of vehicle accelerations. An algorithm is developed for the identification of dynamic vehicle-bridge interaction forces using the vehicle response. Moving force identification theory is applied to a vehicle model in order to identify these dynamic forces between the vehicle and the road and/or bridge. A coupled half-car vehicle-bridge interaction model is used in theoretical simulations to test the effectiveness of the approach in identifying the forces. The potential of the method to identify the global bending stiffness of the bridge and to predict the pavement roughness is presented. The method is tested for a range of bridge spans using theoretical simulations and the influences of road roughness and signal noise on the accuracy of the results are investigated.

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Moving Force Identification: Optimal State Estimation Approach

Cited by 105 publications

References 19 publications

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

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

Improved axle detection for bridge weigh-in-motion systems using fiber optic sensors

A drive-by inspection system via vehicle moving force identification

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