The UK has a long history in the railway industry with a large number of railway assets. Railway bridges form one of the major asset groups with more than 35,000 bridges. The majority of the bridge population are old being constructed over 100 years ago. Many of the bridges were not designed to meet the current network demand. With an expected increasing rate of deterioration due to the increasing traffic loads and intensities, the management authorities are faced with the difficult task of keeping the bridge in an acceptable condition with the constraint budget and minimum service disruptions. Modelling tools with higher complexity are required to model the degradation of assets and the effects of different maintenance strategies, in order to support the management decision making process. This research aims to address the deficiencies of the current bridge condition systems and bridge models reported in the literature and to demonstrate a complete modelling approach to bridge asset management. The degradation process of a bridge element is studied using the historical maintenance data where previous maintenance actions were triggered by a certain type of defects. Two bridge models are then developed accounting for the degradation distributions, service and inspection frequency, repair delay time and different repair strategies. The models provide a mean of predicting the asset future condition as well as investigating the effects of different maintenance strategies will have on a particular asset. The first model is a continuous-time Markov bridge model and is considered more complex than other models in the literature, the model demonstrates the advantages of the Markov modelling technique as well as highlighting its limitations. The second bridge model presented a novel Petri-Net modelling approach to bridge asset modelling. This stochastic modelling technique allows much more detail modelling of bridge components, considering: non-constant deterioration rates; protective coating modelling; limits of the number of repairs can be carried out; and the flexibility of the model allows easily extension to the model or the number of components modelled. By applying the two models on the same asset, a comparison can be made and the results further confirm the validations and improvements of the presented Petri-Net approach. Finally, optimisation technique (Genetic Algorithm) is applied to the bridge models to find the optimum maintenance strategies in which the objectives are to minimise the whole life cycle cost whist maximising the asset average condition. A hybrid optimisation that takes advantage of both bridge models, resulting in a significant time saving, is also presented.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:632488 |
| Date | January 2014 |
| Creators | Le, Bryant Linh Hai |
| Publisher | University of Nottingham |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://eprints.nottingham.ac.uk/14271/ |
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