Quantifying the impact of variability in railway bridge asset managementTools Yianni, P.C., Neves, L.C., Rama, D., Andrews, J.D., Tedstone, N. and Dean, R. (2018) Quantifying the impact of variability in railway bridge asset management. In: The Sixth International Symposium on Life-Cycle Civil Engineering (IALCCE 2018), 28-31 October 2018, Ghent, Belgium. Full text not available from this repository.AbstractBridge asset management is often a challenging and complicated task due to the diversity, multitude and variation of bridge configurations. Much research has been carried out in the field of asset management which has resulted in a wealth of models to help with the decision making process. However, often these models oversimplify the process, resulting in a decision making tool that trivialises the complexity of the decisions. The purpose of this study was to ascertain what the main sources of variability are in the process and then quantify the impact of them in terms of the Whole Life-Cycle Cost (WLCC). The study focuses on human-induced variability to ensure the results are directly influenceable by bridge portfolio managers. The sources of variability identified include misdiagnoses of defects as well as imperfect repairs and variability in costs, all of which are aspects that bridge portfolio managers are exposed to. The sources of variability are quantified and then incorporated into an existing railway bridge WLCC model, established in a previous study, which uses a flexible Petri-Net (PN) approach which is able to incorporate the complex logic and probabilistic aspects. The model itself is able to replicate some of the more complicated decisions which have to be made in bridge asset management, especially those which influence the decisions around rehabilitation. The resulting model, enhanced with the probabilistic variability, is able to reveal the impact of variability on the asset condition, WLCC and even understand where operational complexities are occurring within the system. Incorporating human-induced variability into any model will inevitably increase the financial and operational burden predicted by the model, however, recognising and modelling these aspects is a crucial step in providing bridge portfolio managers a more robust and accurate decision making tool which can more accurately replicate the real world system.
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