Using Petri-Net modelling for a data-driven approach to bridge management and safetyTools Yianni, Panayioti C., Neves, Luís C., Rama, Dovile, Andrews, John and Dean, R. (2017) Using Petri-Net modelling for a data-driven approach to bridge management and safety. In: 9th International Conference on Bridge Maintenance, Safety and Management (IABMAS 2018), 9-13 July 2018, Melbourne, Australia. (In Press) Full text not available from this repository.AbstractBridge portfolio managers have a difficult task managing a large, varied portfolio of aging assets in a squeezed financial climate and on networks with increasing usage. Therefore, decision support tools are becoming more and more integral in managing portfolios of this scale. The model presented in this paper is designed to show a modern, data-driven approach to modelling which would sit as the base for the predictive module of a Bridge Management System (BMS). Extensive research has been carried out in this area however, it has usually resulted in bridge deterioration models; the approach taken in this research is to create a unified system model to encapsulate deterioration, inspection and maintenance to make a Whole Life-Cycle Costing (WLCC) model to offer new insight for bridge portfolio managers. The model in this research uses a Petri-Net (PN) approach which was decided upon as it is flexible enough to incorporate a complex deterioration module, calibrated with over a decade of historic inspection records, corporate policies regarding inspection and maintenance and even certain maintenance practices that are often overlooked. Using the model as a robust foundation, an investigation in the stressors which affect bridge deterioration is carried out. A low-cost, rapid approach to identifying which stressors are driving bridge deterioration is presented. The results indicate which stressors are a safety concern by accelerating the deterioration of bridge assets. Additionally, the results can be used in the PN model to create enhanced deterioration profiles. The resulting model is able to provide more accurate forward prediction capabilities of the assets condition over time, enhancing asset safety predictions.
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