Virtual WIM datasets for the assessment of bridge-specific traffic load effects

Abstract

One of the essential components for the reliability assessment of existing bridges is the collection of Weigh-In-Motion (WIM) observations. These observations provide valuable data on traffic composition, including vehicle loads and individual axle distances. However, at locations where WIM stations are not present, probabilistic predictive models are required to assess the uncertainty in the traffic flows and traffic loads.. In this study, we investigate the use of Gaussian copula-based Bayesian Networks (GCBN) to create a virtual dataset of WIM observations. This dataset is termed “virtual” because it has never been measured at any specific location. Given the uncertainty on inter-vehicle distances, we propose conceptualizing the flow of vehicles as a series of convoys. For traffic composition, vehicle types are sampled from WIM datasets based on the assumption that these datasets represent the variability of vehicle loads. This virtual dataset is then employed to assess the impact of traffic loads on bridges within the Dutch motorway network. Results from the approach utilized confirmed the suitability of the proposed GCBN for generating a virtual dataset that closely reflects the expected traffic composition.