Assessment of the impact of climate change on road maintenance

Climate affects road deterioration, vehicle operating costs, road safety and the environment. Current and past pavement design guides and engineering models assume a static climate whose variability can be determined from past data. This fixed climate assumptions is often used in road management decision support models such as the Highway Development and Management system (HDM-4) to simulate future behaviour of road sections and consequently inform long-term road maintenance strategies and policies. Contrary to the assumption of a static climate in road management approaches, observations over the last 40 or 50 years show increasing trend in global warming. This raises the possibility that the severity and frequency of pavement defects may be altered leading to premature pavement deterioration and increased costs of managing and using roads. As a consequence, current road management strategies and policies may not offer sufficient resilience to increased frequency of future extreme climate events. A study was undertaken at the University of Birmingham to develop improved deterioration model for asphalt rut depth prediction. The approach used entailed the application of Bayesian Monte Carlo analysis. The output of the study will be used to improve existing road management systems such as HDM-4 and to consequently facilitate the investigation of strategies for adapting to future changes in climate

Investigation of the impact of climate change on road maintenance

The performance of roads is known to progressively reduce as a result of separate and interactive effects of climate and traffic. Existing decision support tools such as HDM-4, which are widely used to investigate long-term road maintenance strategies, utilise past climate data instead of future climate predictions. Uncertainties inherent in future climate predictions however imply that application of such tools could lead to outputs that are not robust in light of climate change. The objectives of the study were threefold: firstly, to develop a rut depth prediction model that considered potential effects of future climate; secondly, to formulate a framework for quantification of uncertainties; and finally, to demonstrate the application of the tools developed using a case study. The model was developed using data provided by the UK Highways Agency and UK Climate Impacts Programme. The methodology used was based on Bayesian regression. The developed model was found to perform better than the current asphalt surfacing rut depth model implemented in HDM-4 when future climate data was used. It was concluded that probabilistic outputs from the tools developed including deterioration rates, pavement condition and discounted maintenance costs for each maintenance strategy, and future climate and socio-economic scenarios provide a useful decision making framework for considering alternative strategies for road maintenance on the basis of the level of climate change risks that can be tolerated