建立合理可信的大坝变形监控模型对科学有效地分析大坝变形监测数据和准确可靠地评估大坝工作运行状况意义重大。通过EEMD算法分解大坝变形量,得到代表不同特征尺度的本征模函数(IMF)分量,针对不同IMF分量选择不同影响因素,将各IMF分量作为极限学习机(ELM)的训练样本对大坝变形分量进行分析、拟合、预测,最后累加各IMF分量的预测结果得到大坝变形预测值。以某碾压混凝土重力坝为例,利用EEMD-ELM模型对大坝变形量进行预测,同时与BPNN模型和ELM模型的预测结果进行对比分析,其中EEMD-ELM模型的平均相对误差为0.566,较BPNN模型、ELM模型分别降低54%和14.8%,表明EEMD-ELM模型预测精度更高,具备一定的应用价值。
Abstract
A reasonable and credible dam deformation monitoring model is of great significance for scientific and effective analysis of dam deformation monitoring data and accurate and reliable evaluation of dam's working and operating conditions. The EEMD (Ensemble Empirical Mode Decomposition) model is adopted to decompose the dam deformation monitoring data, and the IMF (Intrinsic Mode Function) components representing different feature scales are obtained. With different influence factors for different components, the IMF components are used as the training samples of ELM (Extreme Learning Machine) to analyze, fit and predict the monitoring data. The predicted values of dam deformation are obtained by adding the values of each component. With a RCC (Roller Compacted Concrete) gravity dam as an example, the prediction result of EEMD-ELM model is compared with those of BPNN (Back Propagation Neural Network) model and ELM model. The comparison result reveal that the prediction accuracy of EEMD-ELM model is higher than that of BPNN model and ELM model, with the mean relative error merely 0.566, 54% and 14.8% lower than those of BPNN and ELM, respectively.
关键词
大坝变形 /
预测模型 /
集合经验模态分解(EEMD) /
极限学习机(ELM) /
本征模函数(IMF)
Key words
dam deformation /
prediction model /
EEMD /
ELM /
IMF
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参考文献
[1] 吴中如, 顾冲时, 苏怀智, 等. 水工结构工程分析计算方法回眸与发展[J]. 河海大学学报(自然科学版),2015,43(5):395-405.
[2] 顾冲时,吴中如. 大坝与坝基安全监控理论和方法及其应用[M].南京: 河海大学出版社,2006.
[3] 戴 波,何 启. 大坝变形监测统计模型与混沌优化ELM组合模型[J].水利水运工程学报,2016(6):9-15.
[4] 汪 程, 杨 光, 祖安君,等. 混凝土坝变形Wavelet-EGM-PE-ARIMA组合预测模型[J]. 长江科学院院报, 2019,36(8):67-72.
[5] TIAN Z S,ZHANG X N,ZHU Q L,et al. Study of BP Neural Network Model to Dam Deformation Monitoring[C]//Proceedings of the Sixth International Conference on Natural Computation, IEEE. Yantai, China, August 10-12, 2010: 1856-1859.
[6] RANKOVIC V, GRUJOVIC N, DIVAC D, et al. Development of Support Vector Regression Identification Model for Prediction of Dam Structural Behaviour[J]. Structural Safety, 2014, 48: 33-39.
[7] HUANG G B, ZHU Q Y, SIEW C K, et al. Predicting Piezometric Water Level in Dams via Artificial Neural Networks[J]. Neural Computing & Applications, 2014, 24(5): 1115-1121.
[8] KANG F, LIU J, LI J, et al. Concrete Dam Deformation Prediction Model for Health Monitoring Based on Extreme Learning Machine[J]. Structural Control & Health Monitoring,2017,24(10):1-11.
[9] 张 豪, 许四法. 基于经验模态分解和遗传支持向量机的多尺度大坝变形预测[J]. 岩石力学与工程学报,2011,30(增刊2):3681-3688.
[10]HUANG N E, SHEN Z, LONG S R, et al. The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis[J]. Proceedings of The Royal Society A Mathematical Physical and Engineering Sciences, 1998, 454(1971):903-995.
[11]WU Z H, HUANG N E. Ensemble Empirical Mode Decomposition: A Noise Assisted Data Analysis Method[J]. Advances in Adaptive Data Analysis, 2009, 1(1): 1-41.
[12]刘 佳. 混凝土坝智能监控模型与软件系统研究[D]. 大连:大连理工大学,2017.
基金
国家重点研发计划项目(2018YFC0407104);国家自然科学基金青年项目(51609074);江苏省基础研究计划青年项目(BK20160872)
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