Decision-Making Method for High-speed Rail Early Warning System in Complex Earthquake Situations

Abstract

Abstract To address the shortcomings in decision-making methods for ground motion threshold warning models in High-speed rail (HSR) earthquake early warning systems (HSREEWs), we propose a dual judgment method and corresponding early warning process for earthquake early warning decisions based on joint peak ground acceleration (PGA) and complex earthquake environmental risk evaluation (ERE) values. First, we analyze the characteristics of four complex earthquake environments based on the characteristics of HSR operating environments. Second, we establish an earthquake environmental risk evaluation index system and propose an adversarial interpretive structure modeling method-based complex earthquake situation evaluation model (AISM-based ESEM). The AISM method firstly evaluates the proximity by the TOPSIS method, then effectively rank targets with fuzzy attributes through opposite hierarchical extraction rules without sacrificing system functionality. Since PGA can reflect the current size of earthquake energy, combining PGA thresholds with ESEM-derived values of ERE can effectively determine the risk status of each train and make decisions on the most appropriate alarm form and control measures for that status. Finally, case analysis results under the background of Wenchuan Earthquake show that the new early warning decision-making method accurately assesses environmental risks in affected areas and provides corresponding warning levels as a supplement to existing HSREEWs warning models.