Bridge weigh in motion (B-WIM) uses accurate sensing systems to transform an existing bridge into a mechanism to determine actual traffic loading. This information on traffic loading can enable ...efficient and economical management of transport networks and is becoming a valuable tool for bridge safety assessment. B-WIM can provide site-specific traffic loading on deteriorating bridges, which can be used to determine if the reduced capacity is still sufficient to allow the structure to remain operational and minimise unnecessary replacement or rehabilitation costs and prevent disruption to traffic. There have been numerous reports on the accuracy classifications of existing B-WIM installations and some common issues have emerged. This paper details some of the recent developments in B-WIM which were aimed at overcoming these issues. A new system has been developed at Queens University Belfast using fibre optic sensors to provide accurate axle detection and improved accuracy overall. The results presented in this paper show that the fibre optic system provided much more accurate results than conventional WIM systems, as the FOS provide clearer signals at high scanning rates which require less filtering and less post-processing. A major disadvantage of existing B-WIM systems is the inability to deal with more than one vehicle on the bridge at the same time; sensor strips have been proposed to overcome this issue. A bridge can be considered safe if the probability that load exceeds resistance is acceptably low, hence B-WIM information from advanced sensors can provide confidence in our ageing structures.
A bridge Weigh-in-Motion (WIM) system consists in estimating gross vehicle weight while they cross a bridge, without any sensor in the pavement. It comprises a set of sensors installed under the ...bridge deck and in bridge elements, and dedicated data analysis tools, allowing to assess the moving loads from the mechanical response of the bridge structure. In this paper, a novel Bridge WIM solution is described, which uses a low number of optical strands strain sensors, and has been implemented on 13 bridges worldwide. The accuracy of this system for gross vehicle weight is assessed on two bridges in France and Italy. The system met the accuracy class A(5) of the COST323 specifications on one bridge. Moreover, the data collected by the system is useful also for the structural health monitoring of the bridge.
In this study, a new, model-free damage detection method is proposed and validated on a simple numerical experiment. The proposed algorithm used vibration data (deck accelerations) and bridge ...weigh-in-motion data (load magnitude and position) to train a two-stage machine learning setup to classify the data into healthy or damaged. The proposed method is composed in its first stage of an artificial neural network and on the second stage of a gaussian process. The proposed method is applicable to railway bridges, since it takes advantage of the fact that vehicles of known axle configuration cross the bridge regularly, that normally only one train is on the bridge at a time and that the lateral positioning of the loads does not change. The novelty of the proposed algorithm is that it makes use of the data on the load’s position, magnitude and speed that can be obtained from a Bridge Weigh-in-Motion system to improve the accuracy of the damage detection algorithm.
► A method of calibrating a Bridge WIM system based in MFI Theory is presented. ► Cross Entropy Opt. is adapted and used to determine parameters of an FE bridge model. ► The algorithm is tested using ...three different FE plate models incorporating VBI.
Moving 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 3-dimensional vehicle model, allowing for Vehicle–Bridge Interaction (VBI).
PDF
