Predictive control method for railway rain-related disasters based on scenario deduction
There are many types of railway rainfall disasters that are very likely to cause railway accidents. The existing single-index monitoring such as rainfall and threshold alarm methods leads to high false alarm rate. To effectively address the key challenges of monitoring and early warning difficulties,inaccurate risk prediction,and the selection of control measures for railway rain-related disasters,this study proposed an innovative predictive control method based on scenario deduction. Initially,based on railway accident reports and the mechanisms causing rain-related disasters,12 types of such disasters were classified into 5 major scenario categories. Subsequently,by using grounded theory,relevant scenario elements for each type were identified and classified,and optimal scenario sets and control strategy sets for each type were constructed using scenario reduction methods,thus forming a scenario network comprising 5 categories. To satisfy the needs of railway disaster prevention decision-makers,two models for disaster planning deduction prediction and real-time deduction prediction were established. A method for converting the scenario network into a disaster Bayesian network was proposed,allowing for precise matching of actual scenarios with the scenario network for deduction prediction,and the selection of appropriate control strategies based on the "scenario-response" method. Finally,a case study of a landslide disaster at Shimiaogou Station was conducted for analysis and verification. The results show that the deduction prediction through this method accurately reflects the development process of the disaster,highly consistent with the actual disaster development situation,with a scenario similarity of 93％ and a disaster occurrence probability of 88％. After implementing corresponding control measures,the probability of the landslide disaster occurrence significantly decreases to 12％,and the possibility of train derailment drops from 70％ to 1％,confirming the accuracy and practicality of this method. This research provides significant theoretical and technical support for the prediction and proactive control of railway rain-related disasters.
万方数据
维普
