A new bridge weigh-in-motion (WIM) algorithm is developed which makes use of strain sensors at multiple longitudinal locations of a bridge to calculate axle weights. The optimisation procedure at the core of the proposed algorithm seeks to minimise the difference between static theory and measurement, a procedure common in the majority of bridge WIM algorithms. In contrast to the single unique value calculated for each axle weight in common Bridge WIM algorithms, the new algorithm provides a time history for each axle based on a set of equations formulated for each sensor at each scan. Studying the determinant of this system of equations, those portions of the time history of calculated axle weights for which the equations are poorly conditioned are removed from the final reckoning of results. The accuracy of the algorithm is related to the ability to remove dynamics and the use of a precise influence line. These issues are addressed through the use of a robust moving average filter and a calibration procedure based on using trucks from ambient traffic. The influence of additional longitudinal sensor locations on the determinant of the system of equations is discussed. Sensitivity analyses are carried out to analyse the effect of a misread axle spacing or velocity on the predictions, and as a result, the algorithm reveals an ability to identify potentially erroneous predictions. The improvement in accuracy of the calculated axle weights with respect to common approaches is shown, first using numerical simulations based on a vehicle-bridge interaction finite-element model, and second using experimental data from a beam-and-slab bridge in Slovenia.

Weigh-in-Motion (WIM) data can be used to predict future traffic volumes and weights for the planning of new infrastructure, the management of maintenance activities, the identification/reduction of overloading problems and the evaluation of the performance of pavements and bridges. Most WIM systems are based on sensors placed in or on the pavement that measure the wheel force applied over them during a very short time. The value of this force varies as a result of road roughness and vehicle dynamics leading to limited accuracy for estimating static weights. Additionally, these systems experience durability problems due to traffic and environmental conditions. An alternative approach to WIM that addresses these limitations is the use of an instrumented bridge to weigh vehicles (B-WIM). This approach is the subject of research in this book. Inaccuracies derived from discrepancies between theoretical B-WIM algorithms and bridge measurements are investigated both theoretically and experimentally. The text also describes the development of a B-WIM system in Ireland, including all aspects of installation, calibration, data collection and its processing into useful traffic information.

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.