Identifying abnormal vehicle subclasses from wim data for the structural design of highway bridges in South Africa

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.