Now showing 1 - 4 of 4
  • Publication
    A general solution to the identification of moving vehicle forces on a bridge
    Bridge Weigh-In-Motion systems measure bridge strain caused by the passing of a truck to estimate static axle weights. For this calculation, they commonly use a static algorithm that takes the bridge influence line as reference. Such a technique relies on adequate filtering to remove bridge dynamics and noise. However, filtering can lead to the loss of a significant component of the underlying signal in bridges where the vibration does not have time to complete sufficient number of cycles, and in cases of closely spaced axles traveling at high vehicle speeds. In order to overcome these limitations and also to provide additional information on the dynamics of the applied forces, this paper presents an algorithm based on first order Tikhonov regularization and dynamic programming. First, strain measurements are simulated using an elaborate 3-D vehicle and orthotropic bridge interaction system. Then, strain is contaminated with noise and input into the moving force identification algorithm. The procedure to implement the algorithm and to derive the applied forces from the simulated strain record is described. Vehicle axle forces are shown to be accurately predicted for smooth and rough road profiles and a range of speeds.
      1993Scopus© Citations 72
  • Publication
    First Order Moving Force Identification Applied to Bridge Weigh-In-Motion
    Bridge Weigh-In-Motion systems are based on the measurement of the deformation of a bridge and the use of these measurements to estimate the attributes of passing traffic loads. Despite many advantages, Bridge Weigh-In-Motion algorithms have often failed to predict axle weights accurately due to noise and vehicle and bridge dynamics. The algorithm in this paper uses Moving Force Identification theory and it applies first order Tikhonov regularization in conjunction with dynamic programming to predict the unknown traffic forces from simulated bridge strain measurements. An accurate finite element mathematical model that resembles the response of the bridge is needed to predict the applied forces. For this purpose, a calibration method based on the Cross-Entropy Optimization algorithm is used to adjust the mass and stiffness matrices of the finite element model. Once the model has been calibrated, the algorithm requires accurate velocity and axle spacing of the vehicle forces and the continuous strain record that they induce on the bridge. Sensitivity analyses are carried out to demonstrate the effect of errors in each of these required inputs. It is shown that the approach proposed herein has the potential to estimate static weights and the time history of the forcing function of each axle accurately.
      196
  • Publication
    Experimental Testing of a Moving Force Identification Bridge Weigh-in-Motion Algorithm
    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 L-curve method. Finally, the static axle loads, impact factors and truck frequencies are obtained from a complete time history of the identified moving forces.
      790Scopus© Citations 52
  • Publication
    A Regularised Solution to the Bridge Weigh in Motion Equations
    The traditional approach to Bridge Weigh-in-Motion (WIM) developed by Moses, gives good accuracy for estimating gross vehicle weights but is less accurate for individual axle weights. In this paper, Tikhonov regularisation is applied to the original Moses’ equations to reduce some of the inaccuracies inherent within the algorithm. The optimal regularisation parameter is calculated using the L-curve criterion. The new regularised solution is numerically tested using simulations of moving vehicles on a bridge. Results show that the regularised solution performs significantly better than the original approach of Moses and is insensitive to road surface roughness.
      1160