Document Type

Theses, Ph.D


Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence


Civil engineering

Publication Details

This thesis was submitted to University College Dublin for the degree of PhD in the College of Engineering, Mathematical and Physical Sciences. The degree of PhD was conferred on the author in July 2010.


This work is based on weigh-in-motion measurements for approximately three million trucks obtained from sites in five European countries. Techniques have been developed, supported by photographic evidence, for filtering the measurements to identify and remove unreliable values, and for the classification of extremely heavy vehicles. The collected measurements have been used as the basis for building and calibrating a Monte Carlo simulation model for bridge loading. Two-lane traffic is simulated – either two lanes in the same direction or one lane in each direction. The model allows for vehicles that are both heavier and have more axles than in the measured data. Careful program design and optimisation have made it practical to simulate thousands of years of traffic. This has a number of advantages – the variability associated with extrapolation is greatly reduced, rare events are modelled, and the simulation output identifies the typical loading scenarios which produce the lifetime maximum loading. Analysis of the measured data shows subtle patterns of correlation in vehicle weights and gaps, both within lanes and between adjacent lanes in same-direction traffic. A new approach has been developed for simulating traffic in two same-direction lanes using flow-dependent traffic scenarios. The measured weights and gaps in the scenarios are modified using variable-bandwidth kernel density estimators. This method is relatively simple to apply and can be extended to more than two lanes. It is shown that the correlation structure in the traffic has a small but significant effect on characteristic maximum loading.