Flexible pavements deform and fatigue under the repeated action of heavy vehicle traffic. Pavement design methods require accurate estimates of traffic loading. Traditionally, vehicle weight has been empirically related to decreased pavement serviceability through the Equivalent Single Axle Load (ESAL) calculated using the ‘fourth-power law’, as determined from the American Association of State Highway Officials (AASHO) Road Test (1958-1960) and codified in the AASHO Pavement Design Guide (Cebon 1999).
ESALs implicitly incorporate a road damage relationship, which is independent of the structure of the road and mode of failure. Many researchers have, therefore, questioned their use (Gillespie et al. 1993; ARA 1999; Cebon 1999). In 1987, the US Long-Term Pavement Performance (LTPP) study began a large-scale field trial to investigate the effects of design and maintenance factors on pavement performance (LTPP 2006). High standard, quality-controlled traffic data has been available from LTPP Special Pavement Studies (SPS) sites since 2006 (LTPPINFO 2009). Data from all LTPP sites was used in the creation and validation of the American Association of State Highway and Transportation Officials (AASHTO) Mechanistic-Empirical Pavement Design Guide (ME-PDG) traffic module, where axle load probability distributions are used to quantify the traffic loading (ARA 1999).
Axle load probability distributions display the probability of the weights of a particular axle or axle group measured at a given site. In the ME-PDG, the pavement distress due to an axle group is calculated using probability distributions and the assumed number of vehicles. This more realistic characterisation of traffic than the traditional ESAL approach is a useful step forward for accurate pavement damage calculations (ARA 1999; Timm et al. 2005; Haider, Harichandran 2007).
Both ESALs and axle load probability distributions assume that the axle loads generated by heavy vehicles are static and therefore constant at all points along the road. In practice, heavy vehicles vibrate in response to rough road surfaces, generating dynamically varying tyre forces. These “dynamic tyre forces” or “dynamic axle loads” are known to be repeatable in space because heavy vehicles often travel at similar speeds with similar payloads, dimensions, suspensions, and tyres (Cole, Cebon 1992; Cole et al. 1996; Collop et al. 1996).
Whole-life pavement response calculations account for repeatable loading by simulating the dynamic response of vehicles to a rough road surface (Collop, Cebon 1995). The challenge of whole-life modelling is to create the correct level of repeatability for the traffic fleet over the lifetime of the road (i.e. millions of vehicles), using a minimum amount of computation time.
This section summarises the study conducted in collaboration with the Engineering Department of the University of Cambridge in order to investigate the available methods for generating repeatable dynamic tyre forces from axle load probability distributions and to determine the most efficient approach to traffic modelling.
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