Petri net modelling of bridge asset management using maintenance-related state conditionsTools Le, Bryant and Andrews, John (2015) Petri net modelling of bridge asset management using maintenance-related state conditions. Structure and Infrastructure Engineering, 12 (6). pp. 730-751. ISSN 1744-8980 Full text not available from this repository.AbstractBridge structures are an important part of the UK transportation network. They are also experiencing increasing rates of deterioration due to the increasing traffic volume and load intensity. Available bridge models have many restrictions due to the assumptions of the analytical method used and the means by which the model states are defined to represent the condition of the structure. These models also lack the complexity to allow detailed maintenance and renewal options to be explored. This paper presents a bridge model developed based on the Petri net (PN) approach. The method allows for detailed modelling of the individual components in the structure whilst maintaining the size of the analytical problem to a manageable size and resulting in an efficient analysis. The bridge model is formed from sub-models of each of the bridge components and takes into consideration the component deterioration process, the interaction and dependency between different component deterioration processes, along with the inspection and maintenance processes. The model states are defined based on actual degraded component conditions which they experience. It is therefore easy to relate these to the appropriate maintenance options. This gives a considerable advantage over those models based on the condition scores or ratings. The state residence times between changes in state resulting from deterioration and maintenance are governed by appropriate Weibull distributions. Thus, avoiding the restriction of constant failure rates used in Markov approaches which are rarely appropriate to model deteriorating asset conditions. The application of the model is demonstrated on a typical bridge structure where the PN model is solved using Monte Carlo simulation, the model results are also presented and discussed.
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