A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. AbstractManagement of a large portfolio of infrastructure assets is a complex and demanding task for transport agencies. Although extensive research has been conducted on probabilistic models for asset management, in particular bridges, focus has been almost exclusively on deterioration modelling. The model being presented in this study tries to reunite a disjointed system by combining deterioration, inspection and maintenance models. A Petri-Net (PN) modelling approach is employed and the resulting model consists of a number of different modules each with its own source of data, calibration methodology and functionality. The modules interconnect providing a robust framework. The interaction between the modules can be used to provide meaningful outputs useful to railway bridge portfolio managers.

A novel approach to comparing bridge deterioration rates under different environmental conditions is employed using a network analysis approach. This approach uses a matrix condition scoring system utilised by Network Rail (NR). It does not require any conversion factors which can introduce subjectivity. A number of different factors were analysed 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 a bridge manager to categorize bridges in different deterioration sets allowing the definition of different optimal inspection and maintenance strategies for each set.

Stochastic Petri-Nets (PNs) are combined with General-Purpose Graphics Processing Units (GPGPUs) to develop a fast and low cost framework for PN modelling. GPGPUs are composed of many smaller, parallel compute units which has made them ideally suited to highly parallelized computing tasks.Monte Carlo (MC) simulation is used to evaluate the probabilistic performance of the system. The high computational cost of this approach is mitigated through parallelisation. The efficiency of different approaches to parallelization of the problem is evaluated. The developed framework is then used on a PN model example which supports decision-making in the field of infrastructure asset management.The model incorporates deterioration, inspection and maintenance into a complete decision-support tool. The results obtained show that this method allows the combination of complex PN modelling with rapid computation in a desktop computer.

Infrastructure assets can be difficult to manage due to the array of defects, the variety of environmental situations and the different operational scenarios. A number of studies have tried to model bridge asset management. The main focus of these models has been on the deterioration profiling as capturing this can be complex. The model presented tries to model railway bridge detrioration as well as the inspection and intervention processes to give a more rounded overview of railway bridge asset management. A Petri-Net (PN) modelling approach is used accompanied by historical data, used to calibrate the deterioration of the model. Industry policies are used to govern the inspection and intervention procedures. Various aspects of the model have been adjusted or enhanced by industry experts. The model is simulated to provide essential outputs for railway bridge portfolio mangers.

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