:Collision, as a common type of ship accidents, leads to serious property loss and personal injury. In this paper, a new framework of quantitative risk assessment is proposed by quantifying the probability and the corresponding consequence based on the historical accident data. Firstly, the consequences of ship collisions are quantified and classified using an equivalent consequence method. Secondly, a decision tree model is established to analyse the impact of ship attributes on the collision consequences. The main ship attributes contributing to collision are determined, based on which, a BP neural network model is developed to estimate the probabilities of the different consequences. Thirdly, the collision risk is predicted by integrating the collision probabilities with the corresponding consequences. Fourthly, a case study in Hong Kong waters is investigated and the results are compared with the available references to validate the proposed framework. The new model can be used to assess present risks to plan preventive measures for the potential collision accidents.
An innovative methodology is proposed to identify potential risk factors and possible accident escalation consequences, and to determine the evolution of an accident from cause to consequence, thereby to identify the most probable path and discover key risk factors along the path rapidly. Based on the principle of a directed weighted complex network (DWCN), the bow-tie (BT) model, risk entropy and the improved ant colony optimization (IACO) algorithm are integrated into this methodology. First, the qualitative analysis of risk evolution based on the BT model is carried out. The evolution development based on accident suppression can be divided into two stages: accident precursor stage and accident evolution stage. Then, a new method for mapping BT into DWCN is proposed. Lastly, the shortest path analysis of risk evolution based on the IACO algorithm is carried out, fuzzy set theory (FST) is introduced to calculate the failure probability of risk factors, and risk entropy is used to represent the uncertainty of risk propagation. Thus, the IACO algorithm can be used to calculate the shortest path of risk evolution. The proposed method is applied to oil and gas leakages in the FPSO oil and gas processing system. The results show that it is an effective method to identify the shortest evolution path and the most vulnerable risk factors.
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