Abstract:
Abnormal vehicle loads pose significant detrimental effects when crossing bridges. By
characterising the traffic load effects experienced on bridges caused by abnormal vehicles,
allows for more reliable bridge design practices. This paper presents an innovative
approach to identify and characterise subclasses of abnormal vehicle types from
weigh-in-motion (WIM) data, by employing Gaussian Mixture Modelling to the load effects.
Each subclass of abnormal vehicles has unique statistical properties and Gaussian
distributions are utilised to determine characteristic load effects and reliability-based partial
factors for each subclass. The aim of this paper is to characterise abnormal vehicles, and
how this information can aid codified bridge design practises in the industry.
