Reliabilities analysis of evacuation on offshore platforms: A dynamicBayesian Network model

An offshore platform is naturally vulnerable to accidents, such as the leakage of dangerous chemicals, fire and explosion because there are a lot of oil and gas, where all the equipment and pipes are squeezed into a limited area. Escape, Evacuation, and Rescue (EER) plans play a vital role as the last barrier to ensure the safety of personnel in the event of a major accident. As a result, the main contributors leading to evacuation failure are analyzed in this study to prioritize technology development needed to select a robust EER strategy. The scope of this research focuses on the quantitative analysis of various EER strategies on offshore platforms. In this research, a reliability prediction model of emergency evacuation is established for offshore platforms based on the K2 structure learning algorithm and a Bayesian network parameter learning method. The conditional probability tables of each node are determined by combining the Bayesian estimation method and a junction tree reasoning engine. The reliability of emergency evacuation on a platform is predicted using a dynamic Bayesian network model. The transition probability is determined through a Markov method. The main factors leading to evacuation failure are investigated using the diagnostic reasoning method of Bayesian Network