基于长期水位记录概率建模的堤内承压水位预测

    Prediction of confined water level within the levee through long-term water level record-based probabilistic modeling

    • 摘要: 本研究旨在实现一种基于长期水位观测数据的预测方法,为T内抗浮设防水位的取值提供依据。依托北江大堤内某工程实例的长期地下水位观测记录及堤外水文资料,以承压水位为研究对象,以经验分布法与极大值分布法实现观测记录的概率建模,外推T内承压水位最大值的密度函数,从而实现承压水远期最高水位的预测。研究以50年重现期承压水位标准值作为预测值,在概率模型与反演法/数值法百年一遇承压水位关联时,50年重现期承压水位标准值分别为10.32 m(经验分布法) 和10.26 m(极大值分布法);在概率模型仅通过观测记录建立时,50年重现期承压水位标准值为9.96 m(极大值分布法)。根据标准值以及观记录分析成果,提出了使用期抗浮设防水位以及施工期抗浮设防水位的取值方法,同时为校准荷载分项系数和组合值系数提供了荷载统计参数。本研究以荷载概率建模作为切入点,解决了抗浮设计的关键问题,为可靠度分析的应用和优化提供依据,技术方法具有较强的兼容性和可操作性,具有一定的推广价值。

       

      Abstract: This study aims to develop a prediction method based on long-term water level observation data to provide a basis for determining the anti-floating safety water level within T. Utilizing long-term groundwater level observation records and hydrological data outside the levee from a project case within the Beijiang levee, the confined water level was taken as the study object. Probability modeling of observation records was implemented through the empirical distribution method and extreme value distribution method, extrapolating the probability density function of the maximum confined water level within T to achieve prediction of long-term peak confined water levels. Using the 50-year return period confined water level standard value as the predictive indicator, the probability model yielded standard values of 10.32 m (empirical distribution) and 10.26 m (extreme value distribution) when calibrated against the 100-year return period confined water level derived from inversion/numerical methods. Conversely, when the probability model was established exclusively from observation records, the extreme value distribution approach gave a standard value of 9.96 m. Based on the standard values and observational analysis results, this study developed determination methods for the anti-floating safety water levels during both service and construction periods, while providing load statistical parameters for calibrating partial factors and combination factors for loads. By adopting probabilistic load modeling as the entry point, this research resolves critical issues in anti-floating design and establishes a basis for applying and optimizing reliability analysis. The technical approach demonstrates strong compatibility and implementability with significant potential for broader application.

       

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