UGMs - Introduction

Over the years, many researchers have studied the complex behaviour of granular materials, using laboratory and in situ testing techniques. An extensive literature review was carried out by Lekarp et al. (2000) to collect findings from previous research and summarize in two companion papers the state of knowledge on resilient and plastic properties of granular materials. In this work the authors point out that these properties are affected by numerous factors such as stress, density, grading, moisture content, stress history, particle shape and load frequency, nonetheless the effect of stress parameters is certainly dominant.
If granular materials are simulated by means of Layered Elastic algorithms or using the Method of Equivalent Thickness, this stress dependency is usually dealt with through iterative processes (Figure 1):
Figure 1: Iterative determination of resilient modulus
The complexity of the problem meant that a large number of models can be found in the literature. An interesting review of available models for the prediction of permanent deformations in UGMs has also been conducted as part of the SAMARIS project by Hornych et al. in 2004.
Hereafter we present a selection of the main approaches to resilient and permanent behaviour of UGMs that might be implemented in the WLPPS.

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Traffic - Introduction

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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Moisture - Introduction

While asphalt layers are heavily affected by temperature, the resilient and permanent behaviour of granular materials are a function of moisture content. A model, therefore, has to be provided to simulate how moisture varies in the pavement and how this affects the performance of the granular layers.

The moisture model presented hereafter is what is used in the ME-PDG, which seems to be most suitable thanks to its simplicity and flexibility. This approach takes into account the effect of moisture by multiplying the resilient modulus of the granular material at optimum moisture content by an environmental factor F_env, which can assume the three forms F_f (for frozen material), F_r (for thawing material) or F_u (for unfrozen material). These factors are function of the moisture content, which is calculated by means of a Soil-Water Characteristic Curve that defines, for a particular material, the relationship between suction and degree of saturation.

In general, water table and moisture contents are considered constant throughout the year if there is no water infiltration into the pavement layers. Nonetheless, there can be cases when the moisture content distribution changes, such as the appearance of full depth cracks or the bursting of a pipe. These are considered very traumatic events for a pavement and it can be very important to simulate how and when they might take place and the amount of damage they might cause. In order to take into account this type of events, a variably saturated flow model is also presented in this paper that allows estimating how moisture content might evolve in different case scenarios. This model is implements the two-dimensional finite difference algorithm discussed by Clement et al. and requires each time step to be solved iteratively by means of a Picard iteration, where each iteration consists in solving a system of linear equations.

Coupling this transient flow model with the F_env approach from the ME-PDG it is possible to estimate the mechanical properties of the granular layers for the critical cases discussed above, enabling the software to consider the presence of weak spots along the pavement that can lead to premature failure.

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Temperature - Introduction

As is well known, the behaviour of asphalt bound materials is extremely temperature dependent. Therefore, a model that estimates temperature profiles in the pavement structure at any particular moment of the pavement’s life is an important part of any predictive tool.
The model that is initially being implemented in this software is based on the generally established Dempsey model. This procedure consists first in calculating an energy balance at the pavement’s surface at any particular time in order to estimate the amount of energy entering (or leaving) the pavement due to radiation and convection, then using a finite differences approach to simulate how heat is transferred through the pavement layers at any particular depth.
The model has been validated against the Mechanical Empirical Pavement Design Guide (ME-PDG) climatic model and against real data collected in the US and available on their Long Term Pavement Performance (LTPP) database.

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