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RCC Dam deformation monitoring index based on maximum entropy and cloud model
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摘要: 变形监控指标是评估和监测大坝安全的重要指标。以高寒地区某碾压混凝土重力坝的挡水坝段为例，首先引入改进的快速Myriad滤波法对大坝变形监测数据进行预处理，然后采用最大熵法和云模型法拟定大坝运行期变形监控指标，最后探讨了异常概率与云模型弱外围元素对定性概念贡献率之间的联系。实例分析表明，虽然云模型法按3En原则拟定的变形监控指标与最大熵法按照异常概率为1%与5%拟定的变形监控指标存在一定差异，但在监测时段内大坝实测变形最大值均小于两种方法拟定的变形监控指标，由于两者均通过数字特征值进行计算，包含的主观成分少，拟定的变形监控指标更可信；采用云模型中的弱外围元素拟定的变形监控指标与异常概率2.15%时的变形监控指标较接近，云模型弱外围元素对定性概念贡献率的计算可以为变形监控指标拟定的异常概率确定提供参考。Abstract: The deformation monitoring index is an important index for evaluating and monitoring dam safety. Firstly, an improved fast Myriad filtering method is applied to pretreat the deformation monitoring data of the typical dam sections of a RCC gravity dam in the alpine region, and then the maximum entropy method and the cloud model method are used to investigate and analyze the monitoring index of deformation during operation period. Finally, the relationship between the abnormal probability and the contribution rate of weak periphery elements of the cloud model to the qualitative concept is discussed in this study. The analysis results show that the monitoring index of deformation investigated by the maximum entropy method differs from that of the cloud model method; moreover, the maximum measured deformation of the dam during monitoring period is less than the index of the deformation prepared by the two methods. Both of them are calculated by numerical eigenvalues, and the subjective components are little, so the monitoring index of deformation is more credible. The monitoring index of deformation of the weak peripheral elements of the cloud model is close to the monitoring index of deformation under the conditions of 2.15% of abnormal probability. It is considered that the calculation of the contribution rate of the weak periphery elements of the cloud model to the qualitative concept can provide a reference for the abnormal probability determination of the monitoring index of deformation.
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Key words:
- RCC dam /
- maximum entropy /
- cloud model /
- monitoring index of deformation /
- abnormal probability
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图 3 坝顶测点原始监测数据与滤波数据对比
Figure 3. Comparison between original monitoring data and filter data of top measuring points
表 1 坝顶测点水平位移极值
Table 1. Extreme situation of top measuring points of horizontal displacement
日期 最大值/mm 2013-10 15.44 2013-11 14.85 2013-12 15.36 2014-01 16.20 2014-02 16.66 2014-03 16.71 2014-04 16.51 2014-05 16.78 2014-06 17.20 2014-07 17.49 2014-08 15.97 2014-09 14.52 2014-10 13.56 2014-11 13.13 2014-12 13.69 2015-01 14.63 2015-02 15.15 2015-03 15.29 2015-04 15.71 2015-05 17.28 2015-06 17.06 2015-07 17.10 2015-08 15.97 2015-09 15.18 2015-10 15.50 
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表 2 数字特征值及函数系数
Table 2. Digital eigenvalues and function coefficients
测点编号 数字特征值 计算结果 函数系数 计算结果 PL5-3 μ0 1.000 0 λ0 -1.202 28 μ1 -2.78E-04 λ1 0.350 62 μ2 0.972 5 λ2 0.205 64 μ3 -0.261 2 λ3 -0.164 74 μ4 2.120 8 λ4 -0.184 59
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表 3 基于云模型的变形指标
Table 3. Deformation index based on cloud model
测点编号 特征值 监控指标/mm Ex En He 3En准则 弱外围元素 PL5-3 15.150 1.336 0.137 19.159 17.823
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[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. WU Zhongru. Safety monitoring theory and its application of hydraulic structures[M]. Beijing: Higher Education Press, 2003. (in Chinese) [2] SU H Z, WEN Z P, WU Z R. Study on an intelligent inference engine in early-warning system of dam health[J]. Water Resources Management, 2011, 25(6): 1545-1563. doi: 10.1007/s11269-010-9760-3 [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. http://slsygcxb.cnjournals.org/ch/reader/view_abstract.aspx?file_no=201606002&flag=1 DAI Bo, HE Qi. A model combining with statistic model and chaos-optimized extreme learning machine for dam deformation monitoring[J]. Hydro-Science and Engineering, 2016(6): 9-15. (in Chinese) http://slsygcxb.cnjournals.org/ch/reader/view_abstract.aspx?file_no=201606002&flag=1 [4] 罗德河, 郑东健.大坝变形的小波分析与ARMA预测模型[J].水利水运工程学报, 2016(3): 70-75. http://slsygcxb.cnjournals.org/ch/reader/view_abstract.aspx?file_no=201603009&flag=1 LUO Dehe, ZHENG Dongjian. Wavelet analysis and ARMA prediction model for dam deformation[J]. Hydro-Science and Engineering, 2016(3): 70-75. (in Chinese) http://slsygcxb.cnjournals.org/ch/reader/view_abstract.aspx?file_no=201603009&flag=1 [5] 李萌, 包腾飞, 杨建慧, 等.灰色模型改进的大坝变形分形几何监控模型[J].水利水运工程学报, 2016(4): 104-110. http://slsygcxb.cnjournals.org/ch/reader/view_abstract.aspx?file_no=201604015&flag=1 LI Meng, BAO Tengfei, YANG Jianhui, et al. Fractal geometry monitoring model for dam deformation based on improved grey model[J]. Hydro-Science and Engineering, 2016(4): 104-110. (in Chinese) http://slsygcxb.cnjournals.org/ch/reader/view_abstract.aspx?file_no=201604015&flag=1 [6] 丛培江, 顾冲时, 谷艳昌.大坝安全监控指标拟定的最大熵法[J].武汉大学学报(信息科学版), 2008, 33(11): 1126-1129. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=SciencePaper201303040000112430 CONG Peijiang, GU Chongshi, GU Yanchang. Maximum entropy method for determining dam safety monitoring indices[J]. Geomatics and Information Science of Wuhan University, 2008, 33(11): 1126-1129. (in Chinese) http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=SciencePaper201303040000112430 [7] 朱凯, 秦栋, 汪雷, 等.云模型在大坝安全监控指标拟定中的应用[J].水电能源科学, 2013, 31(3): 65-68. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=QKC20132013042300016560 ZHU Kai, QIN Dong, WANG Lei, et al. Application of cloud model in determining dams safety monitoring index[J]. Water Resources and Power, 2013, 31(3): 65-68. (in Chinese) http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=QKC20132013042300016560 [8] LI D Y, LIU C Y, GAN W Y. A new cognitive model: cloud model[J]. International Journal of Intelligent Systems, 2009, 24(3): 357-375. doi: 10.1002/int.v24:3 [9] SHAO M, NIKIAS C L. Signal processing with fractional lower order moments: stable processes and their applications[J]. Proceeding of the IEEE, 1993, 81(7): 986-1010. doi: 10.1109/5.231338 [10] YUE B, PENG Z M, HE Y M, et al. Impulsive noise suppression using fast Myriad filter in seismic signal processing[C]//Expanded Abstracts of 5th International Conference on Computational and Information Science(ICCIS), 2013: 1001-1004. http://dl.acm.org/citation.cfm?id=2548936 [11] GOH B M K, LIM H S. Sequential algorithms for sample Myriad and weighted Myriadfilter[J]. IEEE Transactions on Signal Processing, 2012, 60(11): 6047-6052. doi: 10.1109/TSP.2012.2208959 [12] 黄耀英.大坝安全监控正反分析方法与优化调控[M].北京:中国水利水电出版社, 2016. HUANG Yaoying. Forward and back analysis methods and optimal control of safety monitoring in dams[M]. Beijing: China Water & Power Press, 2016. (in Chinese) [13] JAYNES E T. Information theory and statistical mechanics[J]. Physics Rev, 1957, 106(4): 620-630. doi: 10.1103/PhysRev.106.620 [14] 龚纯, 王正林.精通MATLAB最优化计算[M].北京:电子工业出版社, 2012. GONG Chun, WANG Zhenglin. Proficient in MATLAB optimization calculation[M]. Beijing: Publishing House of Electronic Industry, 2012. (in Chinese) -
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