A dynamic domino effect risk analysis model for rail transport of hazardous material

Rail transport of hazardous material (RTHM) plays a vital role in the supply chain of raw materials and products. However, RTHM can pose severe risks due to the large quantities of flammable and explosive chemicals transported over rail tracks crossing residential and industrial areas and possible human and technical failures. Among the potential safety issues, the domino effect is one of the most feared events, which can have devastating consequences despite its relatively low probability. As the first study, the present investigation develops a dynamic risk analysis model for analyzing domino effects in RTHM based on Dynamic Bayesian Network. Accident scenarios such as pool fire, flash fire, fire ball, vapor cloud explosion, and BLEVE are considered to analyze domino effects. The model performance is tested on a real RTHM (i.e., gasoline transportation), demonstrating the effectiveness of the proposed model in simulating the domino-driven effects in terms of both consequences and probability escalation and in dealing with the parameter and model uncertainties.

Automated summary

Rail transport of hazardous material is crucial in the supply chain, but poses severe risks, with the domino effect being a major concern. This study introduces a dynamic risk analysis model based on Dynamic Bayesian Network to analyze domino effects in RTHM, demonstrating its effectiveness through real-world testing with gasoline transportation.