引水隧洞在长期服役过程中受物理化学耦合作用导致结构性能劣化。针对现有安全评价方法多依赖定性指标和经验判据, 难以实现结构安全状态量化评估的问题, 提出了一种基于实时反演和变形演化的隧洞结构安全评价方法。该方法首先对隧洞原始变形监测序列进行数据预处理, 获取高质量的时效变形数据; 然后采用基于深度学习和Bayes优化的物理信息引导的反演方法获取当前隧洞力学参数。在此基础上, 基于内变量热力学理论下的弹-黏塑性-损伤蠕变本构模型和对材料的蠕变行为特征分析, 构建了包含长期变形加速结构安全系数S₁、变形非线性起始结构安全系数S₂和短期变形加速结构安全系数S₃的隧洞3S结构安全评价指标体系, 并阐释了各指标的物理意义。为模拟材料劣化效应, 通过设定不同降强系数表征材料性能的渐进衰减, 开展多工况数值模拟, 揭示了隧洞结构随劣化程度加剧的变形演化规律。将该分析评价框架应用于JLL隧洞工程, 结果表明, S₁为2.4~3.0, S₂为3.7~4.6, S₃为5.7~7.3, 量化了不同劣化程度对应的结构安全临界状态, 验证了该方法的可行性。

Objective: With the continuous expansion and increasing complexity of water diversion tunnels in hydropower projects, their long-term structural safety has become a critical engineering challenge. Conventional safety evaluation methods often rely on qualitative assessments or multi-index systems, which are highly subjective and fail to adequately account for progressive material deterioration and time-dependent deformation. This study proposes an integrated quantitative framework that combines real-time inversion of mechanical parameters and deformation evolution analysis to dynamically evaluate the structural safety of tunnels. Methods: The proposed framework integrates four major components: data preprocessing, parameter inversion, deformation simulation, and safety evaluation. First, the raw deformation monitoring data are preprocessed by imputing missing values using the K-nearest neighbors (KNN) algorithm, identifying and correcting outliers with a sliding-window Z-score method, and reducing noise through logarithmic trend fitting. Second, a physics-informed inversion approach combining deep learning architectures—fully connected layers (FCL) and gated recurrent units (GRUs)—with Bayesian optimization is established to infer the current mechanical parameters of the tunnel from preprocessed deformation data. Third, an elastoviscoplastic damage creep constitutive model based on internal variable thermodynamics is employed to simulate deformation behavior under various material-degradation scenarios, represented by different strength reduction coefficients (K_r). Finally, based on the analysis of material creep behavior and deformation evolution patterns, a time-dependent "3S" safety evaluation index system is established. This system comprises the long-term deformation acceleration safety coefficient (S₁), the nonlinear deformation initiation safety coefficient (S₂), and the short-term deformation acceleration safety coefficient (S₃). The safety state of the tunnel structure is quantified according to the relevant deformation evolution characteristics using the proposed 3S safety coefficients. The physical implications of these indices are as follows: S₁ characterizes the critical point at which the structure transitions into an accelerated creep phase under continuous strength attenuation, indicating the long-term instability risk; S₂ reflects the onset of deviation from the linear response during initial deformation, marking the beginning of dominance by nonlinear mechanical behavior; and S₃ indicates the threshold for notable acceleration of deformation within a defined short-term period, serving as an indicator of potential sudden instability. Results: The proposed method was implemented in the JLL Tunnel, a 20km-long underground structure located in Hunan Province, China, which features complex geological conditions. The mechanical parameters were successfully inverted from field monitoring data, with simulated deformation curves showing high agreement with the measured values. Numerical simulations under different K_r conditions revealed distinct deformation patterns. For K_r≥0.7, deformation stabilized after initial convergence. When 0.4≤K_r≤0.6, deformation exhibited slow growth, followed by an acceleration phase. For K_r≤0.3, deformation accelerated rapidly within a short time. The computed 3S safety coefficients were S₁=2.4-3.0, S₂=3.7-4.6, and S₃=5.7-7.3, indicating that the tunnel is currently in a safe state with sufficient safety margins. These results validated the method's effectiveness in distinguishing between short- and long-term risks and in providing early safety warnings through deformation trajectory analysis. Conclusions: This study proposes an integrated quantitative framework for tunnel structural safety evaluation that effectively combines real-time monitoring data, physics-based modeling, and deformation evolution analysis. The established 3S index system provides a refined insight into structural behavior under material degradation and enables safety assessment across multiple time scales. Compared with conventional methods, the proposed framework enhances objectivity, supports the dynamic prediction of time-dependent performance, and facilitates lifecycle safety management and preventive maintenance of tunnel structures. The methodology demonstrates strong generalizability and offers remarkable practical value for risk prevention and sustainable operation in tunnel engineering.