Unsealed road pavement management: Surface condition deterioration and sustainability modelling

Abstract

Many countries have large unsealed road networks and although they carry low traffic volumes, they require substantial budgets for maintenance and contribute significantly to dust pollution, loss of resources, sedimentation and road accidents. Poor condition unsealed roads affect accessibility, productivity and economic growth, particularly in rural areas.In Australia around 500,000 km (60% of the total road network) of public roads are unsealed, 80% of these are under the jurisdiction of local councils, they generate around 60 t of dust per year, have an asset value of more than $15 billion, and almost 20% are in a poor or very poor condition.The assessment of sustainability has developed over the last decade from environmental management to reporting that includes a full range of sustainability aspects. This has led to the development of a large number of procedures and tools to quantify a number of sustainability outcomes, typically categorised as social, environmental and economic. However, these developments were primarily related to sealed roads and none are available for unsealed roads.A review of the literature shows that the management of unsealed roads is a major challenge. Although the implementation of formal pavement managements systems has proven to be an effective tool in the management of unsealed roads, the application on unsealed road networks has been limited. Surface condition prediction models are important components of these pavement management systems and cover roughness, gravel loss, shape loss, skid resistance, erosion, dust and passability (surface stability). Some of the main reasons for the limited use of formal pavement management systems are concerns about the accuracy of the surface condition prediction models in the systems, the rapid changing condition of unsealed roads, and the complexity of the available systems.The objective of the research is to address two of the current needs in the design and management of unsealed roads, i.e. the quantification of sustainability outcomes and the simplification of the use of surface condition prediction models in the management of unsealed road pavements.The research entailed an overview of unsealed roads, sustainability, unsealed road pavement design and management, and surface condition prediction models; detailed reviews of the surface condition prediction models and their relationship with material classification systems; and the development of sustainability outcome models and a simplified approach to the use of surface condition prediction models in pavement management.A number of surface condition deterioration models (TRRL, HDM-4, Australian, South African and New Zealand) are available. The research assessed the models in detail and investigated the relationship with material classification systems and design recommendations. These models vary significantly in the prediction of outcomes due to the different climates in which they were developed, the inaccuracies in the measurement of the condition properties and the parameters used in the models. The two main condition properties affecting maintenance are roughness (which determines the blading needs) and gravel loss (which determines the re-gravelling needs). Roughness was found to be strongly related to traffic and rainfall, but not to wearing course material properties, while gravel loss is also strongly influenced by the material properties. The commonly used material classification systems and categories (AASHTO, the Unified Soil Classification System and the South African categories) were found not to be effective in predicting gravel loss.The use of these models in pavement management is questioned in light of the inaccuracies and variability. However, the models do have merit and can effectively be used to determine blading frequencies and re-gravelling needs if used appropriately.The sustainability outcome models were developed based on currently used economic analysis procedures and relationships, surface condition prediction models, and cover social (surface friction), environmental (loss of resources, dust, emissions) and economic (vehicle operating, accident costs and time costs) aspects. These models can be used in the comparison of the sustainability outcomes of different pavement and wearing course material options, and the inclusion of sustainability outcomes in the evaluation of re-gravelling frequencies and resealing options.Relationships were developed using traffic, rainfall and blading cost to determine the blading frequencies for two scenarios, i.e. maintaining the network at below a certain roughness, or with cost as criterion.Two of the most commonly used surface condition prediction models (Australian and South African) were simplified by using only traffic, rainfall and plastic factor to develop appropriate re-gravelling frequencies.