Statistical vehicle classification methods derived from girder strains in bridges

This paper investigates the use of a pre-existing network of resistive strain gauges located on the girders of a single bridge span to determine the classification and estimate the weight of vehicles traveling over that span. Vehicle events on the bridge are identified automatically by a measurement filtering algorithm. Manual classification labels are then applied to a subset of these events to investigate the strain signal features that distinguish various vehicle classes. Trends in these features over time are investigated, and an estimate of vehicle weight is obtained from these features without the need for detailed knowledge of the structure's composition. Additionally, a number of neural network configurations are tested on the problem of determining vehicle class from these features. Results are tested on data from both the summer and winter seasons. Finally, estimates of vehicle weight are improved by using the classification network to filter input events.