Vehicle axle loads identification using finite element method

Law, S.S.; Bu, Jian Qing; Zhu, Xinqun; Chan, Siu Lai

doi:10.1016/j.engstruct.2004.03.017

Cited by 102 publications

(58 citation statements)

References 14 publications

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“…Zhu and Law [11] utilise an orthogonal function method in the identification of moving loads. Law et al [12] use a finite element method to identify the axle loads and a condensation technique to reduce the system to a smaller number of master degrees of freedom. For a more detailed review of the state of the art in MFI, see Yu et al [13].…”

Section: Introductionmentioning

confidence: 99%

Adaptation of Cross Entropy optimisation to a dynamic Bridge WIM calibration problem

Dowling

O’Brien

González

2012

Engineering Structures

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Publication information Engineering Structures, 44 (44): 13-22Publisher Elsevier Item record/more information http://hdl.handle.net/10197/4858 Publisher's statementThis is the author's version of a work that was accepted for publication in Engineering Structures. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Engineering Structures (44, , (2012) AbstractMoving Force Identification (MFI) theory can be used to create an algorithm for a Bridge Weigh-in-Motion (WIM) system that can produce complete force histories of the loads that have traversed a bridge structure. MFI is based on general inverse theory, however, and calibration of such a system requires a complete Finite Element (FE) model of the bridge to be available for implementation in the field. This is something that is often infeasible in practice as FE models created using theoretical values for material properties bear a poor relation to reality. The Cross-Entropy optimisation method has been adapted here to address this calibration problem. The general system FE global mass and stiffness matrices of the bridge FE model are found by best fit optimisation to match field measurements. In this fashion a fully automated calibration procedure is developed for an MFI algorithm. This system is tested theoretically using three different FE plate models, coupled with a threedimensional vehicle model, allowing for Vehicle Bridge Interaction (VBI).

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Section: Introductionmentioning

confidence: 99%

Adaptation of Cross Entropy optimisation to a dynamic Bridge WIM calibration problem

Dowling

O’Brien

González

2012

Engineering Structures

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“…However it has been considered in only a small proportion of moving force identification problems (Chan et al 1999, Law et al 2004, Pinkaew 2006, Deng and Cai 2010b, Wu and Law 2011. In this paper, the vehicle-bridge interaction is modelled as a coupled system as the solution is given at each time step and no iteration is required in the computational process.…”

Section: Coupled Vehicle-bridge Interaction Modelmentioning

confidence: 99%

“…Yu and Chan (2007) provide a comprehensive literature review of this research. Methods which utilise the MFI technique can typically be categorised into one of two groups: (a) those which use discrete bridge models and the finite element (FE) method (Chan and Yung 2000, Law et al 2004, Pinkaew 2006 Asnachinda 2007) and (b) those which are based on an exact solution method including a form of system identification (Law et al 1997, Chan et al 1999, Yu and Chan 2003a.…”

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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“…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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Vehicle axle loads identification using finite element method

Cited by 102 publications

References 14 publications

Adaptation of Cross Entropy optimisation to a dynamic Bridge WIM calibration problem

Adaptation of Cross Entropy optimisation to a dynamic Bridge WIM calibration problem

A drive-by inspection system via vehicle moving force identification

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

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