Risk assessment for long-distance gas pipelines in coal mine gobs based on structure entropy weight method and multi-step backward cloud transformation algorithm based on sampling with replacement

Abstract

Destruction of pipelines because of coal mine gob disasters may result in enormous financial loss and significantly affect public safety. Hence, risk assessment of gob pipelines is of immense significance for field personnel. Given the lack of statistical data and the limitations of expert experience, a prior single risk value is often insufficient to reflect the actual situation and cannot meet the needs of the site. The backward cloud transformation (BCT) algorithm is a method that can fully mine the local information contained in expert experience to restore the overall information. However, the existing BCT algorithm has no solution under certain conditions, which considerably limits its application. This study proposes a comprehensive risk assessment method by combining the structural entropy method with a multi-step backward cloud transformation algorithm based on sampling with replacement (MBCT-SR). First, a simplified model for rapid identification is used to determine whether it is worth calculating risk values. Second, a fault tree that fits the actual situation is established, and the weights of the indexes are determined by the structural entropy weight method. Third, the interval scores of the indexes are transformed into numerical features of the cloud model, which are then logically operated using virtual cloud algorithms. Finally, the risk values of the pipeline can be obtained, the cloud droplet diagram of the risk values is clearly shown by the forward cloud transformation (FCT) algorithm, and the risk level can be obtained by the probability that the cloud droplet falls into each risk level interval. To validate the utility of the proposed method, a case study of a coal mine gob around a long-distance gas pipeline was investigated.