Hierarchical asphalt pavement deterioration model for climate impact studies

Abstract

Quantification of the impacts of projected climate change on road pavement performance is possible using predictive models that correctly consider key causal factors of pavement deterioration. These factors include climate, traffic, properties of materials and the design of pavements. This paper presents a new model developed to predict rutting in asphalt surfacing. In addition to the key causal factors of road deterioration, the developed model takes into account several sources of uncertainties, particularly those inherent in future climate change predictions and model input parameters. The asphalt surfacing rut depth progression model was developed from a hierarchical road network data structure using a Bayesian regression approach resulting in a model for each surfacing group. The model was applied within a Monte Carlo simulation framework to derive probabilistic outputs of pavement rut depth progression and maintenance costs under the pre-determined future climate scenarios. This model is useful for application at both the network and project levels to develop road management strategies and policies.

Keywords::

• pavement deterioration models
• Bayesian regression
• climate change
• rut depth
• climate impact assessment