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As extreme events continue to increase both in magnitude and frequency as a result of climate change, capturing the impacts of these is crucial for decision makers to plan in the short, medium, and long term. There is not much that can be done to prevent the occurrence of extreme weather events, however by predicting the consequences of failure in a multi-modal and multiscale transport system and understanding how that failure propagates with respect to time, and loss of functionality of the asset/system, the consequences could be minimised. There are different failure absorption patterns that can occur when a failure occurs and in the recovery phase, depending on the system's preparedness. However, there is a lack of clear understanding of the pattern of each of these failure absorption and recovery trajectories, as it can show the level of functionality of the transport network post-disaster and hence its performance level. This PhD project aims to address this gap. A comprehensive literature review was conducted, which showed there are very few research studies that investigate different failure propagation patterns as part of the consequence. All the studies have focused on failure in the form of a sudden drop in performance, which for some assets is true but is not always necessarily true. Therefore, in this study, different failure propagation patterns (i.e., trigonometric) are considered for different assets and a sensitivity analysis for a variety of single and multi-hazard scenarios are conducted using different performance indicators in the transport system and investigate the sensitivity of the failure to different modelling assumptions. This poster will include the methodology being used and preliminary results of investigating the range of existing patterns identified in the literature to understand their impact on the prediction of failure consequences. |
