Comparison of two independently developed bridge weigh-in-motion systems

O’Brien, Eugene J.; Žnidarič, Aleš; Dempsey, A T

doi:10.1504/ijhvs.1999.054503

Cited by 45 publications

(30 citation statements)

References 4 publications

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“…However, the ratio between the two is actually reducing. For 2000 trucks per day over 250 working days per year over a return period of 1000 years, the characteristic loading event is that with a probability of 1 in (2000×250×1000 =) 500×10 6 . This corresponds to a probability of exceedence of 1 -1/(500×10 6 ).…”

Section: Assessment Dynamic Ratio (Adr)mentioning

confidence: 99%

“…It is being developed as part of the ARCHES research project [4] which involves partners from Belgium, Croatia, Czech Republic, Ireland, Italy, The Netherlands, Poland, Slovenia, Spain and Switzerland, and is partfunded through the European Commission's 6 th Framework research programme. Soft Load Testing is based on bridge Weigh-In-Motion (WIM) measurements [5][6][7][8], where instrumented bridges are used to determine the static weight of vehicles as they pass overhead. Recent Bridge WIM algorithms include 'measured' influence lines [9] which give an accurate relationship between load effect and static vehicle/axle weight.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Characteristic dynamic traffic load effects in bridges

O’Brien

Rattigan

González

et al. 2009

Engineering Structures

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When formulating an approach to assess bridge traffic loading with allowance for Vehicle-Bridge Interaction (VBI), a trade-off is necessary between the limited accuracy and computational demands of numerical models and the limited time periods for which experimental data is available. Numerical modelling can simulate sufficient numbers of loading scenarios to determine characteristic total load effects, including an allowance for VBI. However, simulating VBI for years of traffic is computationally expensive, often excessively so. Furthermore, there are a great many uncertainties associated with numerical models such as the road surface profile and the model parameter values (e.g., spring stiffnesses) for the heavy vehicle fleet. On site measurement of total load effect, including the influence of VBI, overcomes many of these uncertainties as measurements are the result of actual loading scenarios as they occur on the bridge. However, it is often impractical to monitor bridges for extended periods of time which raises questions about the accuracy of calculated characteristic load effects.Soft Load Testing, as opposed to Proof Load or Diagnostic Load Testing, is the direct measurement of load effect in bridges subject to random traffic. This paper considers the influence of measurement period on the accuracy of soft load testing predictions of characteristic load effects, including VBI, for bridges with two lanes of opposing traffic. It concludes that, even for relatively short time periods, the estimates are reasonably accurate and tend to be conservative. Provided the data is representative, Soft Load Testing is shown to be a useful tool for calculating characteristic total load effect.

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Section: Assessment Dynamic Ratio (Adr)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Characteristic dynamic traffic load effects in bridges

O’Brien

Rattigan

González

et al. 2009

Engineering Structures

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show abstract

“…Although the deviations with respect to the static response that vehicle and bridge dynamics introduce in the measured response tend to be averaged out during the minimization process, they remain a significant source of inaccuracy [2]. This paper proposes an alternative B-WIM algorithm that calculates the time history of moving forces as they cross the bridge, based on moving force identification (MFI) theory.…”

Section: Moving Force Identification Bridge Weigh-in-motion Algorithmmentioning

confidence: 99%

Experimental Testing of a Moving Force Identification Bridge Weigh-in-Motion Algorithm

et al. 2008

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Bridge weigh-in-motion systems are based on the measurement of strain on a bridge and the use of the measurements to estimate the static weights of passing traffic loads. Traditionally, commercial systems employ a static algorithm and use the bridge influence line to infer static axle weights. This paper describes the experimental testing of an algorithm based on moving force identification theory. In this approach the bridge is dynamically modeled using the finite element method and an eigenvalue reduction technique is employed to reduce the dimension of the system. The inverse problem of finding the applied forces from measured responses is then formulated as a least squares problem with Tikhonov regularization. The optimal regularization parameter is solved using the Lcurve method. Finally, the static axle loads, impact factors and truck frequencies are obtained from a complete time history of the identified moving forces.Keywords: Bridge, Weigh-in-motion, Force identification, Regularization, Dynamic programming, Traffic loads Moving Force Identification Bridge Weigh-in-Motion AlgorithmThe traditional static bridge weigh-in-motion (B-WIM) algorithm provides static axle weights from minimizing the sum of squares of differences between measured total bridge strain and theoretical static strain [1]. Although the deviations with respect to the static response that vehicle and bridge dynamics introduce in the measured response tend to be averaged out during the minimization process, they remain a significant source of inaccuracy [2]. This paper proposes an alternative B-WIM algorithm that calculates the time history of moving forces as they cross the bridge, based on moving force identification (MFI) theory. The MFI algorithm implemented here was developed by the authors [3][4][5], and is an extension of the one-dimensional algorithm by Law and Fang [6] to two dimensions. The mathematics behind general inverse theory is available in the literature [7][8][9][10][11]. The MFI algorithm requires a finite element (FE) mathematical model that accurately represents the static and dynamic behavior of the bridge structure. The method of dynamic programming requires that the equilibrium equation of motion be converted to a discrete time integration scheme. In this case the equilibrium equation of motion is reduced to an equation in modal coordinates defined by:where [Φ] is the modal matrix of normalized eigenvectors and n z is the number of modes to be used in the inverse analysis.[Ω] is a diagonal matrix containing the natural frequencies and ζ is the percentage damping. [L(t)] is a time varying location matrix relating the n g applied vehicle forces of the vector g(t) to the degrees of freedom, n dof , of the original FE model. Tikhonov regularization [12] is applied to provide a bound to the error and 'smoother' solutions to the ill-conditioning nature of the MFI problem [13,14]. The final part of the solution lies in the calculation of the optimal regularization parameter; the L-curve has been chosen to obtain...

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“…Conventional algorithms can then be used to determine axle and gross vehicle weights. The original algorithm of Moses [1] and variations thereon have been tried and tested in numerous applications [2][3][4][5]. In conventional The concept of a Nothing-On-Road (NOR) B-WIM system, in which only transducers placed beneath the bridge are used to identify axles, is a recent one.…”

Section: Introductionmentioning

confidence: 99%