In today’s modern world, society are accustomed to disposable products, temporary services and frequent replacements. The art of maintaining and renewing assets has been somewhat lost. However, in the pursuit of financial performance, comes the need to effectively manage assets. Management of a large portfolio of infrastructure assets is a complex and demanding task for infrastructure owners. Not only is the coordination of a large organisation difficult to align, but every decision is scrutinised by regulatory bodies. For infrastructure portfolio managers, decision support tools are becoming increasingly more useful. This is particularly relevant to railway structures as a result of their diversity and age. A thorough literature review (Chapter 2) is carried out to understand what decision support tools, known as Bridge Management Systems (BMSs), are currently available for railway bridge portfolio managers. The modelling approaches which have been used as the foundation of the BMSs are analysed (Chapter 3). Of these, the most appropriate modelling technique is selected for development of a new approach for a decision support tool. The tool comprises of a number of different modules, each with its own characteristics, data sources and features (Chapter 4). The model is presented, as well as detailed descriptions of each of the modules and how they work. During the literature review stage, a number of studies mentioned that there are external factors that affect deterioration. However, very few studies were able to pinpoint what these factors were, how much they affected deterioration and what the operational, financial and management impacts were. To that effect, a number of different factors were analysed (Chapter 5) to ascertain if they have an effect on bridge deterioration. The key factors were identified and their deterioration profiles incorporated into a probabilistic Petri-Net (PN) model, calibrated with historical data. From these, comparative model outputs pinpointing which factors affect bridge deterioration the most can be computed. Finally, simulations were carried out on the PN model to evaluate which of the factors would have the most financial effect for a transport agency. This allows bridge managers to categorize bridges in different deterioration groups allowing the definition of different optimal inspection and maintenance strategies for each group. This research has also identified that complex models often have a heavy computational burden. A study was carried out to accelerate simulations of PN models with General-Purpose Graphics Processing Units (GPGPUs)(Chapter 7). GPGPUs are composed of many smaller, parallel compute units which has made them ideally suited to highly parallelized computing tasks. The efficiency of different approaches to parallelization of the problem is evaluated. The developed framework is then used on the railway bridge PN model. The results obtained show that this method allows the combination of complex PN modelling with rapid computation in a desktop computer. A final piece of research was undertaken to perform optimisation with the railway bridge PN model (Chapter 8). This study utilised the foundation railway bridge PN model, the Local Environmental Factors (LEFs), the variability factors and the GPGPU acceleration. A Hybrid Multi-Objective Genetic Algorithm (MOGA) approach is accelerated with GPGPUs to find the optimal inspection regime to minimise both the WLCC of railway bridges and the risk of being in a poor condition. The proposed Hybrid Genetic Algorithm (GA) approach is able to accelerate the process by over 30 times compared to the traditional GA approach. The results obtained demonstrate a potential 9% reduction in overall WLCC for UK railway bridges at the same condition as the current industry policy performance. A novel Performance-Based Inspection Planning (PBIP) protocol is introduced to demonstrate where inspections should be focused to monitor bridges in areas susceptible to more severe deterioration whilst easing inspection efforts on those in milder areas of deterioration, improving operational efficiency.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:719645 |
| Date | January 2017 |
| Creators | Yianni, Panayioti C. |
| Publisher | University of Nottingham |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://eprints.nottingham.ac.uk/42984/ |
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