高心墙堆石坝赋存地质与环境复杂,防渗结构渗透破坏引发的结构故障甚至溃坝现象时有发生,合理评估大坝渗透破坏风险十分关键。针对高心墙堆石坝渗透系数时变规律不清晰、渗透破坏时变风险模型刻画难度大的问题,基于有限元模拟和原观数据,构建了高心墙堆石坝与地基渗透系数反演代理模型,提出了渗透系数的时变规律及表征函数;基于渗透系数时变规律和Monte-Carlo法,构建了高心墙堆石坝渗透破坏时变风险分析模型,并以瀑布沟大坝为案例进行了验证。结果表明:瀑布沟大坝坝与地基渗透系数反演的平均相对误差为0.4%,精度较高,且心墙与覆盖层的渗透系数均呈递增趋稳的时变规律;大坝渗透破坏时变可靠指标β在4.33~5.37之间,β值均大于目标可靠指标,表明该工程渗透破坏风险低,与工程实际相符。

[Objective] The material composition of high core rockfill dams is complex. Under the influence of hydraulic loading, temperature, and other complex environmental factors during long-term service, the permeability coefficients of these materials inherently exhibit time-varying characteristics, significantly influencing seepage stability and overall dam safety. This study aims to address the limitations of existing research, which predominantly focuses on static parameter inversion or non-time-varying risk assessment, and lacks systematic consideration of the time-varying patterns of material parameters and the influence of long-term operation. [Methods] A time-varying risk analysis method for seepage failure in high core rockfill dams was proposed, integrating data decomposition, finite element simulation, time-varying parameter inversion, and failure risk analysis. First, based on multi-year measured seepage pressure data of the dam, empirical mode decomposition was used to extract the periodic and trend components. Combined with orthogonal experiments and response surface methodology, an inverse surrogate model for the permeability coefficients of the high core rockfill dam and its foundation was constructed. Subsequently, through optimization using a genetic algorithm, this study investigated and revealed the time-varying patterns and characterization functions of permeability coefficients. Finally, based on the time-varying patterns of permeability coefficients and the Monte Carlo method, a time-varying risk analysis model for seepage failure in high core rockfill dams was established to achieve the dynamic risk assessment of seepage failure in the dam structure. [Results] This method was applied to the Pubugou Dam project. The results showed that the relative error of permeability coefficient inversion for both the dam and foundation was less than 2%, with an average relative error of 0.4%. Seepage field simulations based on the inverted parameters showed that the distribution of seepage pressure inside the dam followed the rising and falling trend of the reservoir water level and was consistent with the patterns observed in the measured seepage pressure data. This conformed to the typical seepage field distribution patterns of high core rockfill dams, indicating a high level of inversion accuracy. Furthermore, the permeability coefficients of both the core wall and the overburden layer showed time-varying patterns of gradual increase and stabilization. Reliability analysis of seepage failure in the dam and its foundation indicated that the reliability indicator (β) of the dam consistently exceeded the design target value during the operational period, suggesting that the overall risk of seepage failure was low. Additionally, the reliability indicator for seepage failure in the dam and its foundation exhibited periodic fluctuations with changes in the reservoir water level, showing a generally negative correlation between the reliability indicator and reservoir water levels and a positive correlation between failure probabilities and water levels. This was generally consistent with the seepage characteristics and patterns of dam structures under different water level conditions, validating the applicability of the proposed time-varying risk analysis model. The results confirmed that the reliability indicator for seepage failure of the Pubugou Dam complied with regulatory requirements. [Conclusions] The method developed in this study integrates time-varying parameter inversion, modeling of time-varying patterns, failure path search, surrogate model construction, and time-varying risk analysis. By dynamically identifying and updating time-varying parameters in real time, it enables accurate simulation of seepage failure processes and full lifecycle monitoring of risk evolution, thereby enhancing the timeliness and accuracy of safety risk assessments for high dam structures.