Bridge Weigh in Motion

Traffic loads on the highway bridges have been increasing in the past years. Actual truck axle configurations and weights may deviate significantly from standard legal design trucks depending on States, freight corridors, and seasons. Changes in legal truckloads are often made by state legislatures based on the pressure by the trucking industry. It is important to understand how trucks impact bridges and whether they cause deterioration and degradation so that policy decisions on legal truckloads can be based on scientific facts. The information of the actual passing vehicles that would be required to address such concerns includes actual truck axle loads and configurations, speeds, and dynamic effects. The critical questions are: How can we measure truck axle configurations and weights over the long term? How can we identify and measure the corresponding critical bridge responses over the long term, and how can we relate these to changes in the bridge life cycle? Finding objective answers to these questions requires complex research. This study is a first step through exploring bridge weigh-in-motion (B-WIM) algorithms, which help to capture the truck's information for instrumented bridges in operations. Several B-WIM algorithms were inspected first and classified based on their assumptions in incorporating dynamic bridge vehicle interaction. For instance, B-WIM algorithms can be classified as static and dynamic. For the static case, the dynamic effect caused by the interaction between vehicle and bridge is mostly ignored, which results in significant inaccuracies when the dynamic component of the coupled systems is not insignificant or challenging to filter out. Therefore, this study mainly focuses on dynamic algorithms. Various dynamic algorithms, such as dynamic programming and augmented Kalman filters (AKF), have been investigated for their challenges and opportunities they offer into B-WIM applications. Various techniques were employed to address the potential deficiency of dynamic algorithms. These techniques include modal superposition technique to deal with complex structures, mode truncation, singular value decomposition to mitigate the singularity of the formulation matrices, and dummy measurements to improve the identification process's observability. The influence of different parameters on the performance of AKF based B-WIM algorithms were investigated via a parametric study of a simply supported beam subject to moving forces. The results show that those parameter has been carefully chosen for the success of the force prediction. A B-WIM framework was developed. The framework is composed of all the components required to estimate the vehicular weights all the way up from conceptualizations. Critical ones include structural identification to prepare the digital twin, eigenvalue reduction to reduce the dimension of the problem, the numerically verified AKF algorithm, and a parameter tuning method based on optimization technique. A scale model inspired by a typical highway bridge span was designed and constructed in the laboratory following the Similitude Theory. It aims to demonstrate the capability of the proposed B-WIM framework. Multiple tests with different vehicular speeds, weights, and moving paths were conducted and analyzed. Complete force-time histories were predicted and discussed. For B-WIM, the vehicular weights were calculated by averaging the force-time history segment where the vehicle was entirely on the bridge, and results show good agreement between the predicted forces and the static weights.