Risk-accident emergence and prevention method of high-speed railway system: based on an improved Functional Resonance Analysis Method

An improved Functional Resonance Analysis Method (FRAM) is proposed to analyze the risk-accident emergence of high-speed railway (HSR) system. By integrating Work Breakdown Structure (WBS) and Risk Breakdown Structure (RBS), we decompose operational processes and identify risk factors, constructing a FRAM functional network to model interactions among functional modules. Key innovations include: (i) quantifying functional variability using risk pulse energy, (ii) calculating variability transmission probability via site percolation theory, and (iii) integrating causal emergence theory to link micro-level functional resonance with macro-level accident occurrence. A case study of the 7.23 Ningbo-Wenzhou railway accident validates the method, revealing that four critical functional modules exhibit high resonance intensity due to coupled risk factors, leading to accident emergence. By constructing micro/macro Markov transition matrices and calculating Effective Information (EI), we show that risk-accident emergence occurs when macro-level EI exceeds micro-level EI, indicating nonlinear causal effects in the system. The proposed method enables quantitative analysis of accident evolution pathways and guides targeted prevention: reducing variability transmission probability, enhancing information transmission efficiency, and improving module resistance to risk coupling. These findings provide a data-driven framework for proactive safety management in HSR systems, addressing limitations of traditional qualitative FRAM approaches.