基于"数据-机理"驱动的土石坝渗流性态分析与分级预警研究OA

Seepage pressure monitoring data are an important indicator for conducting safety prediction and early warning of reservoir dams.However,the internal structure of earth-rock dams,soil conditions,and other operational factors are complex and easily affected by external factors such as rainfall and reservoir water levels,making it difficult to obtain accurate data.This paper aimed to analyze the influencing factors and correlation relationships of seepage behavior in reservoir dams,constructed an analytical warning model,and enhanced the ability to analyze,diagnose,and provide safety warnings for dam operation forms.The paper was based on long-term monitoring data of correlation factors such as seepage pressure and reservoir water level.Following the idea of"data governance,regression analysis,and interval extraction",multiple methods such as box plot,K-means clustering,and polynomial regression were used to integrate data governance and analysis,extract confidence intervals and prediction intervals.By constructing a"data-mechanism"driven dam seepage pressure grading warning model,an earth rock dam safety grading warning system was established,and the effectiveness of the research method and model was verified using the Qianshanmen Reservoir as an example.The research results indicated that this method had high accuracy and reliability in fitting the upstream water level seepage pressure curve,which was of great significance for the safe operation of reservoir dams.