Monitoring models for base flow effect and daily variation of dam seepage elements considering time lag effect (Open Access)
Monitoring models for base flow effect and daily variation of dam seepage elements considering time lag effect (Open Access)
复制标题
考虑时滞效应的基流效应和大坝渗流元素日变化的监测模型（开放获取）
DOI：
10.1016/j.wse.2018.12.004
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发表时间：
2018
影响因子：
4
通讯作者：
Bao Teng-fei
中科院分区：
文献类型：
--
作者：
Wang Shao-wei;Xu Ying-li;Gu Chong-shi;Bao Teng-fei
受外部环境因素和大坝性能演变的影响，大坝渗流性状表现出非线性时变特征。为了预测和评价大坝渗流性态的长期发展趋势和短期波动，建立了基流效应和渗流要素日变化两种监测模型。在第一种模型中，为避免时滞效应对渗流要素时效分量评价渗流变化的影响，采用小波多分辨率分析方法提取渗流要素和库水位的基值，并通过建立的基流效应监测模型分离时效分量。在建立大坝渗流要素日变化监测模型时，考虑了大坝渗流要素监测时间之前的实测时间序列对监测结果可能产生的影响。首先将与所分析的渗流要素正相关的因子作为支持向量机（SVM）模型的输入因子，然后进行基于SVM核函数的灵敏度分析，优化输入因子集，建立优化的日变化SVM模型。通过对某混凝土重力坝坝坡下两个测压管水位的实例分析，验证了两种模型的有效性和合理性。对优化后的SVM模型进行灵敏度分析，结果表明，上游库水位日变化和降雨量对测压管水位日变化的影响是服从正态分布的过程。
Affected by external environmental factors and evolution of dam performance, dam seepage behavior shows nonlinear time-varying characteristics. In this study, to predict and evaluate the long-term development trend and short-term fluctuation of the dam seepage behavior, two monitoring models were developed, one for the base flow effect and one for daily variation of dam seepage elements. In the first model, to avoid the influence of the time lag effect on the evaluation of seepage variation with the time effect component of seepage elements, the base values of the seepage element and the reservoir water level were extracted using the wavelet multi-resolution analysis method, and the time effect component was separated by the established base flow effect monitoring model. For the development of the daily variation monitoring model for dam seepage elements, all the previous factors, of which the measured time series prior to the dam seepage element monitoring time may have certain influence on the monitored results, were considered. Those factors that were positively correlated with the analyzed seepage element were initially considered to be the support vector machine (SVM) model input factors, and then the SVM kernel function-based sensitivity analysis was performed to optimize the input factor set and establish the optimized daily variation SVM model. The efficiency and rationality of the two models were verified by case studies of the water level of two piezometric tubes buried under the slope of a concrete gravity dam. Sensitivity analysis of the optimized SVM model shows that the influences of the daily variation of the upstream reservoir water level and rainfall on the daily variation of piezometric tube water level are processes subject to normal distribution.