Pavements represent an important infrastructure to all countries. In Saudi Arabia, huge investments have been made in constructing a large network. This network requires great care through conducting periodic evaluation and timely maintenance to keep the network operating under acceptable level of service. Pavement distress prediction and pavement condition prediction models can greatly enhance the capabilities of a pavement management system. These models allow pavement authorities to predict the deterioration of the pavements and consequently determine the maintenance needs and activities, predicting the timing of maintenance or rehabilitation, and estimating the long range funding requirements for preserving the performance of the network. In this study, historical data of pavement distress and pavement condition on the main and secondary road network of Riyadh, Saudi Arabia were collected. These data were categorized, processed, and analyzed. These data have been employed to generate prediction of pavement distress and condition models for the Saudi Arabia Urban Road Network (SAURN). Throughout the study, the most common types of pavement distress on SAURN have been identified. The behavior of these distress types has been investigated. A sigmoid function was found to be an excellent representation of the data. Seven for urban main pavement distress models (UMPDM) have been developed. In addition, six urban secondary pavement distress models (USPDM) have been developed. Moreover, two pavement condition models have also been developed, one for urban main pavement condition (UMPCM), and the other for urban secondary pavement condition (USPCM). The developed models provide a reasonable prediction of pavement condition. The models were assessed by standard error and residual analysis. A suitable procedure for the implementation of the models has also been proposed.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:523863 |
| Date | January 2010 |
| Creators | Mubaraki, Muhammad |
| Publisher | University of Nottingham |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://eprints.nottingham.ac.uk/11171/ |
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