## 1 Introduction
For example, [PERSON] and [PERSON] [10] used AHP and EWM to calculate the subjective and objective weights of risk indices. They then applied a game theory-based combined weighting approach and used index-weight weighting to assess urban disaster risks. [PERSON] et al. [11] used FAHP and EWM to calculate subjective and objective weights, adopted the minimum standard deviation method to determine optimal combination coefficients, and performed an urban flood risk assessment using the resulting integrated weights. [PERSON] et al. [12] proposed a comprehensive weighting algorithm that integrates AHP, the maximum absolute weighted residual method, and the maximum entropy method to evaluate risks associated with three typical signaling devices in railway systems. [PERSON] et al. [13] used AHP to calculate subjective weights without incorporating objective weighting. [PERSON] et al. [14] used AHP, EWM, and the game theory combined weighting method (GTCWM) to calculate subjective, objective, and combined weights. Both studies [13; 14] recognized the importance of accounting for the prominent influence of high-risk indices. However, when using AHP for subjective weighting, a consistency test is required, which adds complexity to the calculation process. Despite these limitations, the methodological innovations in these studies, particularly in weight optimization and the treatment of high-risk factors,offer valuable references for developing risk assessment models tailored to railway track maintenance operations. Current research on risk assessment for railway track maintenance operations faces three major challenges: (1) insufficient spatiotemporal precision, which makes it difficult to accurately identify the spatial location and temporal point of risk occurrences, thereby reducing the effectiveness and timeliness of early warning systems; (2) limited scenario coverage in evaluation frameworks--most existing studies focus on a single type of maintenance operation--result in poor generalizability across diverse operational contexts; and (3) inadequate assessment methodologies: conventional AHP approaches rely heavily on subjective judgment and require complex consistency checks, while FCE methods using linear weighting often underestimate the influence of high-risk factors. To address these challenges, this study proposes improvements in three key areas: (1) integration of spatiotemporal information--by embedding spatiotemporal data into the assessment process, a refined risk assessment model is developed that enables precise identification of both the location and timing of risk occurrences; (2) development of a cross-scenario risk index system--a comprehensive index system is constructed to enhance the model's applicability across various maintenance scenarios, improving its versatility; and (3) implementation of a nonlinear FCE model with integrated weighting--a nonlinear FCE approach is proposed that combines subjective and objective weighting methods. Specifically, the G1 method and EWM are used in conjunction with a nonlinear weighting operator to enhance the model's sensitivity and accuracy in evaluating high-risk factors. The structure of the remainder of this paper is as follows: Section 2 presents the core methodologies and theoretical foundations underlying the research. Section 3 details the construction process of the proposed risk assessment model for track maintenance operations. Section 4 applies the model to a case study involving track maintenance operations on the Lanzhou-Xinjiang Railway to verify its practical effectiveness. Section 5 discusses the model's accuracy, advantages, and limitations and proposes corresponding countermeasures. Section 6 provides a comprehensive summary of the research findings.