常规大坝安全监控统计模型未能分别针对监测序列值内系统信号和随机信号特点进行模拟,故预报精度存在一定的提升空间。基于小波分解技术,利用监测序列值信号频率特征分离出系统信号与随机信号,并结合逐步回归与支持向量机(SVM)对不同信号的处理优势,在引入网格寻优与交叉验证确定SVM敏感参数的基础上,提出了一种基于多元统计结合小波分解和支持向量机的大坝位移监控模型,同时编制了其相应的计算程序。工程算例表明,该模型较常规模型能够同时考虑监测序列中的系统信号和随机信号,并且具有较强的模型寻优能力和更高的预报精度,从而验证了所建模型的有效性,该方法亦可推广应用于高边坡及大坝其他预警指标的监控。
Abstract
The systematic signal and random signal in the monitoring sequence are difficult to distinguish in the conventional monitoring models of the dam, thus the forecasting accuracy of the conventional model can be promoted. In this paper, we separate the systematic signal from random signal by their frequency features based on wavelet decomposition. According to the advantages of managing signals of stepwise regression and Support Vector Machine(SVM), in association with grid search and cross validation methods for determining the sensitive parameters of SVM, we present a monitoring model of dam displacement based on multivariate statistical combined with wavelet decomposition and support vector machine. Then the calculating procedures are compiled. The engineering examples indicate that both the systematic signal and random signal can be separated effectively in the composite model, with high forecasting accuracy and good optimization ability. Finally, the composite model is effective and the method can be applied to high slope monitoring and other warning indicators of dam projects.
关键词
大坝位移 /
小波分解 /
参数寻优 /
支持向量机 /
监控模型
Key words
dam displacement /
wavelet decomposition /
parameter optimization /
support vector machine /
monitoring model
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参考文献
[1] 吴中如. 水工建筑物安全监控理论及其应用[M]. 北京: 高等教育出版社, 2003.
[2] 汪树玉, 刘国华, 杜王盖. 大坝观测数据序列中的混沌现象[J]. 水利学报, 1999, (7): 22-26.
[3] STOJANOVIC B, MILIVOJEVIC M, IVANOVIC M, et al. Adaptive System for Dam Behavior Modeling Based on Linear Regression and Genetic Algorithms[J]. Advances in Engineering Software, 2013, 65(10): 182-190.
[4] 许 昌, 岳东杰, 董育烦, 等. 基于主成分和半参数的大坝变形监测回归模型[J]. 岩土力学, 2011, 32(12): 3738-3742.
[5] 李子阳, 郭 丽, 顾冲时. 大坝监测资料的时变Kalman预测模型[J]. 武汉大学学报(信息科学版), 2010, 35(8): 991-995.
[6] 王雪红,刘晓青,陶海龙,等.优化BP神经网络的位移预测模型[J].水利水运工程学报,2014,(2):38-42.
[7] 朱凤林, 韩 卫. Matlab 仿真平台下大坝位移BP神经网络模型研究[J]. raybet体育在线
院报, 2013, 30(1): 99-101.
[8] 缪新颖, 褚金奎, 杜小文. LM-BP神经网络在大坝变形预测中的应用[J]. 计算机工程与应用, 2011, 47(1): 220-222.
[9] BURRUS C S , GOPINATH R A , GUO H. Introduction to Wavelets and Wavelet Transforms: A Primer[M]. Englewood Cliffs, United States: Prentice Hall, 1997.
[10]李端有, 周元春, 甘孝青. 混凝土拱坝多测点确定性位移监控模型研究[J]. 水利学报, 2011, 42(8): 981-985,994.
[11]李 波, 顾冲时, 李智录, 等. 基于偏最小二乘回归和最小二乘支持向量机的大坝渗流监控模型[J]. 水利学报, 2008, 39(12): 1390-1394,1400.
[12]丁世飞, 齐丙娟, 谭红艳. 支持向量机理论与算法研究综述[J]. 电子科技大学学报, 2011, 40(1): 2-9.
[13]徐洪钟, 吴中如, 李雪红, 等. 基于小波分析的大坝变形观测数据的趋势分量提取[J]. 武汉大学学报(工学版), 2003, 36(6): 5-8.
基金
国家自然科学基金项目(51569014,51409139)
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