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.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:556833 |
| Date | January 2011 |
| Creators | Anyala, Michael |
| Publisher | University of Birmingham |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://etheses.bham.ac.uk//id/eprint/2815/ |
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