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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An experiment is described in which two independently developed Bridge Weigh-inMotion (WIM) systems are tested and compared both for accuracy and durability. The systems, one a prototype Irish one still under development, the other a commercially available American one, were tested on a bridge in Slovenia. Eleven statically pre-weighed trucks were each driven over the bridge several times at a range of typical highway speeds. Accuracy's for axle and gross vehicle weights are presented within the framework of the draft European WIM specification and the bias which can be introduced by the selection of calibration truck demonstrated. Performance factors relating to durability are also discussed with particular emphasis on axle detectors.

This paper develops a novel method of bridge damage detection using statistical analysis of data from an acceleration-based bridge weigh-in-motion (BWIM) system. Bridge dynamic analysis using a vehicle-bridge interaction model is carried out to obtain bridge accelerations, and the BWIM concept is applied to infer the vehicle axle weights. A large volume of traffic data tends to remain consistent (e.g., most frequent gross vehicle weight (GVW) of 3-axle trucks); therefore, the statistical properties of inferred vehicle weights are used to develop a bridge damage detection technique. Global change of bridge stiffness due to a change in the elastic modulus of concrete is used as a proxy of bridge damage. This approach has the advantage of overcoming the variability in acceleration signals due to the wide variety of source excitations/vehicles—data from a large number of different vehicles can be easily combined in the form of inferred vehicle weight. One year of experimental data from a short-span reinforced concrete bridge in Slovenia is used to assess the effectiveness of the new approach. Although the acceleration-based BWIM system is inaccurate for finding vehicle axle-weights, it is found to be effective in detecting damage using statistical analysis. It is shown through simulation as well as by experimental analysis that a significant change in the statistical properties of the inferred BWIM data results from changes in the bridge condition.

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...