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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Welcome

Predicting how a pavement will perform under certain levels of traffic and climatic conditions is extremely important for designers and planners, representing possibly one of the most complicated tasks and certainly the ultimate target of most of the research being done in the field of pavement engineering.

In order to address this research topic, software is being developed to predict Long Term Pavement Performance (LTPPS) that will have the characteristics of being modular and open-source. This is a very large and ambitious project that involves the Universities of Nottingham and Cambridge and significant additional funding for some of the detailed model development and programming has been obtained from the New South Wales Road Traffic Authority and the Nottingham Asphalt Research Consortium.

Each module will be dedicated to a particular aspect of pavement design, such as micro-climate, resilient and plastic behaviour of materials, damage calculation etc., and will be designed in such a way to have standard inputs and outputs in order to be interchangeable with other similarly built modules.

The concept behind this design is that this platform will create a community of researchers that will use this common framework to develop, exchange and use modules related to their own area of expertise, contributing to the diffusion and application of knowledge in a much more efficient way than before.

This section describes the characteristics and implementation of novel software for predicting long term pavement performance and is the continuation of the work presented in the previous deliverable of the ASSET-Road project DEL 4.1. For the purpose of clarity it will be appropriate to summarise briefly the reasoning behind this part of the project and the concepts that have led to the current stage of the tool.

Given the very high costs involved during both construction and maintenance phases and the very large environmental, economical and social impact that a road has on the communities that it serves, one of the main challenges that researchers, planners and designers in the field of pavement engineering have always faced is the prediction of how that road would perform throughout its service life under variable conditions of traffic and environment. For this reason in the past decades a number of ever more sophisticated models (numerical, analytical or empirical) have been developed by researchers and practitioners and have often been implemented into software packages of various complexities. These tools can range from a simple analysis of a few sections along the road under some standard load conditions to more detailed simulations of numerous points along the road with dynamic load and environmental conditions updated daily or monthly. It is easy therefore to see how in some cases computational power and efficiency can be vital to the outcome of a project and hence it is not only important to develop reliable analytical models but also to implement them in the best possible way.

As one can understand, software can only be considered as good as the analytical models it uses, and once these become obsolete due to the advancements in the field usually the programs that use them would need to be replaced or updated. In order to address this issue that can be seen as a limitation of the currently available software packages, a program is being developed within the Work Package 4 of the ASSET-Road project to predict Long Term Pavement Performance (LTPPS) that will have the characteristics of being modular and open-source. This is a very large and ambitious project that involves the Universities of Nottingham and Cambridge and significant additional funding for some of the detailed model development and programming has been obtained from the New South Wales Road Traffic Authority and the Nottingham Asphalt Research Consortium.

The idea behind this approach is to supply a fully functional software that can be used to design new roads or plan maintenance on existing ones while at the same time allowing users and researchers to modify it in order to implement at any moment in time the models that are considered more appropriate for certain situations. By doing this it is possible to deliver a tool built to withstand the test of time, where advancements in the field of pavement engineering can be applied instantly and new models and approaches can be compared to older ones. The vision is that of a global community of users that will develop new modules for the software and share them online in such a way that the software will effectively become a platform where all new knowledge and state of the art models will be made available to everyone, hence speeding up the dissemination and the adoption of new ideas.

Keeping in mind these main objectives, it is evident how much of the effort in the development of this software needs to be dedicated to the software structure itself. Although the various modules that the software will run have the important role of constituting the most visible part of the software, allowing it to output reliable results since the very first release, they still represent a “temporary” part of the program that will eventually be replaced by new modules designed by the user and plugged on the original framework of the software, therefore the main focus of this research project has been the development of a software architecture that will allow the user to customise every aspect of the simulation in an intuitive way.

For this reasons the next sections will be dedicated first of all to the customisation of the Graphical User Interface and the extension of this concept to a customisable framework, with a glance at the creation of an online user’s community, then to the description of the particular modules employed in the current version of the program and finally to some applications and practical examples.

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