SPAAHP

To enhance Expressway's response to sudden accidents and improve maintenance resilience, this paper introduces safety resilience theory into high-speed section maintenance safety management. Considering highway features and based on resilience theory's five characteristics, key factors for high-speed maintenance safety resilience are identified using Word Frequency Analysis (WFA). The nonlinear mapping between system classification and highway characteristics is pivotal. Subjective and objective weights of maintenance safety resilience indices are weighted using the Interval Analytic Hierarchy Process Critical (IAHP-CRITICAL) method and Minimum Information Identification Principle, addressing traditional IAHP's fuzziness. The Improved Dung Beetle Algorithm Extreme Learning Machine (DBO-ELM) is employed for comprehensive evaluation. The WFA-CRITICAL-IAHP-DBO-ELM model is applied to a Chang-Zhang Expressway section.