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.

Bridge weigh-in-motion system (B-WIM) testing is a popular technology in bridge applications. The B-WIM system can track extensive information about loading conditions to which bridges are subjected, and engineers can evaluate the responses of bridges and assess their performance relative to the safety index and serviceability. FAD (Free-of-Axle-Detector) or NOR (Nothing-On-Road) B-WIM system works well, but only if the system detects axle locations. In the USA, there are challenges for some beam-and-slab bridges. In the first manuscript, we describe a study with alternative strategies for sensor types and sensor installation locations for beam-and-slab bridges. The sensor layouts are identified and two new sensors are investigated. Most of the commercially available B-WIM systems are based on an algorithm developed by Moses (1979). The performance of this method is acceptable for estimating gross vehicle weight (GVW), but it can be unsatisfactory for estimating single axle loads. In order to improve the accuracy to an acceptable level, two algorithms are proposed. The second and third manuscripts present the measurement of axle weights and GVWs of moving heavy vehicles based on these algorithms. As determined in a case study of a bridge on US-78, both algorithms significantly improved the accuracy of measurements of axle weights in comparison with the commercial B-WIM system. Existing bridges may be functionally obsolete or have deficient structures based on older design codes or features. These bridges are not unsafe for normal vehicle traffic, but they can be vulnerable to specific traffic conditions. We propose, in manuscript 4, use of a simulation model based on B-WIM experimental data derived during extreme events. The results provide an improved understanding of the possible deficiencies of this bridge, and an appropriate retrofit is suggested. Finally, the dynamic amplification factor (DAF) is a significant parameter for design new of bridges and for evaluation of existing bridges. AASHTO guidelines provided very conservative values. So, improved methods for determination of DAF values need to be developed to evaluate the safety of existing bridges. This manuscript presents a simulation method to evaluate the DAF of existing bridges by use of the B-WIM data. The accurate results are obtained based on site-specific data.