Large-scale hybrid Bayesian network for traffic load modeling from weigh-in-motion system data

Traffic load plays an important role not only in the design of new bridges but also in the reliability assessment of existing structures. Weigh-in-motion systems are used to collect data to determine traffic loads. In this paper, the potential of hybrid nonparametric Bayesian networks (BNs) is demonstrated for modeling the complex data measured by the weigh-in-motion systems. The quantification process provides insight into the statistical buildup of the traffic load. The BN is shown to be a reliable traffic load model for use in bridge design. The model's value is shown with applications for prediction of missing data and calculation of extreme loads. A simulation that includes both a dynamic BN and a static component is performed. The model is able to generate the distribution function of section forces, such as bending moments, generated by multiple vehicles in several lanes. The model presented in this paper should serve as a benchmark for further applications