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Research on a Seepage Monitoring Model of a High Core Rockfill Dam Based on Machine Learning

机译:基于机器学习的高芯堆石坝渗流监测模型研究

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摘要

The seepage of a rockfill dam with a high core wall is an important and difficult issue in the safety monitoring of a core rockfill dam, something about which managers are immensely concerned. Seepage of a high core rockfill dam is mainly affected by factors such as water level, rainfall, temperature, filling height, and aging. The traditional research method is to establish a multiple linear regression model to analyze the influence factors of seepage. However, the multicollinearity between these factors affects parameter estimation, and random errors in the data cause the regression model to fail to be established. This paper starts with data collected by an osmometer, uses the 3δ criterion to process the outliers in the sample data, uses the R language to perform principal component analysis on the processed data to eliminate the multicollinearity of the factors, and finally uses multiple linear regression to model and analyze the data. Taking the Nuozhadu high core rockfill dam as an example, the influencing factors of seepage in the construction period and the impoundment period were studied and the seepage was then forecasted. This method provides guidance for further studies of the same type of dam seepage monitoring model.
机译:高芯墙的堆石坝的渗漏是核心堆石坝安全监控中的一个重要而困难的问题,管理者对此非常关注。高芯堆石坝的渗流主要受水位,降雨,温度,填土高度和老化等因素的影响。传统的研究方法是建立多元线性回归模型来分析渗流的影响因素。但是,这些因素之间的多重共线性会影响参数估计,并且数据中的随机错误会导致无法建立回归模型。本文从渗压计收集的数据开始,使用3δ准则处理样本数据中的离群值,使用R语言对处理后的数据进行主成分分析以消除因素的多重共线性,最后使用多元线性回归对数据进行建模和分析。以糯扎渡高芯堆石坝为例,研究了施工期和蓄水期的渗流影响因素,并对渗流进行了预测。该方法为进一步研究同一类型的大坝渗流监测模型提供了指导。

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