Optimized prediction model for concrete dam displacement based on signal residual amendment
Optimized prediction model for concrete dam displacement based on signal residual amendment
复制标题
基于信号残差修正的混凝土坝位移优化预测模型
DOI：
10.1016/j.apm.2019.09.046
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发表时间：
2020-02-01
影响因子：
5
通讯作者：
Wang, Gang
中科院分区：
文献类型：
--
作者：
Wei, Bowen;Chen, Liangjie;Wang, Gang
传统的混凝土坝位移监测统计模型在水利工程中得到了广泛的应用。然而，由于信息挖掘方法陈旧，泛化能力弱，传统的计算模型预测精度较差。此外，残差序列中隐含的不确定混沌效应也是难以建模的。针对大坝时间序列混沌特性的非线性、时变性和不稳定性，采用多尺度小波技术对多元回归模型残差进行分解和重构。通过自回归积分滑动平均（ARIMA）模型的线性训练能力完成低频自相关部分的拟合预测，并构建支持向量机（SVM）回归模型对非线性高频信号进行优化处理。在此基础上，建立了基于信号残差修正的混凝土坝位移组合预测模型。工程实例分析表明，该组合模型能较好地识别原型监测信号的时频非线性特征，提高了模型的拟合精度、抗噪能力和鲁棒性。此外，本文还对所建立的组合数学模型进行了改进和发展，可应用于其它水工建筑物影响量的预测分析。(C)2019爱思唯尔公司All rights reserved.
The traditional statistical model of concrete dam's displacement monitoring is used widely in hydraulic engineering. However, the forecasting precision of the conventional calculation model is poor due to the antiquated method of information mining and weak generalization capacity. Furthermore, the uncertain chaos effect implied in residual sequence is also intractable for modeling. In consideration of the nonlinearity, time variation, and unsteadiness of the chaotic characteristics of a dam time series, multiscale wavelet technology is used to decompose and reconstruct the residuals of multiple regression models. The fitting prediction of the low-frequency autocorrelation part is completed through the linear training ability of the autoregressive integrated moving average (ARIMA) model, and the support vector machine (SVM) regression model is constructed to optimize and process the nonlinear high-frequency signal. Then, a combined forecasting model for concrete dam's displacement based on signal residual amendment is established. The analysis of an engineering example indicates that the combined model built in this study can identify the time-frequency nonlinear characteristics of the prototype monitoring signal well, thus improving its fitting precision, antinoise ability, and robustness. In addition, the combined mathematical model established in this study is improved and developed for application to the prediction analysis of the effect quantities of other hydraulic structures. (C) 2019 Elsevier Inc. All rights reserved.