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472 工程科学学报,第44卷.第3期 (顾冲时,苏怀智,王少伟.高混凝土坝长期变形特性计算模型 based approach diagnosing structural behavior of dam engineering 及监控方法研究进展.水力发电学报,2016,35(5):1) Struct Control Health Monitor,2018,25(2):e2073 [3]Li MC,Ren Q B,Kong R,et al.Dynamic modeling and prediction [14]Yu H,Wu Z R,Bao T F,et al.Multivariate analysis in dam analysis of dam deformation under multidimensional complex monitoring data with PCA.Sci China Technol Sci.2010.53(4): relevance.J Hydraul Eng,2019,50(6):687 1088 (李明超,任秋兵,孔锐,等.多维复杂关联因素下的大坝变形动 [15]He J P.Theory and Application of Dam Safety Monitoring 态建模与预测分析.水利学报,2019,50(6):687) Beijing:China Water Power Press,2010 [4]Dong DD,Zu A J,Sun X L.Model of dam deformation (何金平,大坝安全监测理论与应用.北京:中国水利水电出版 monitoring based on genetic ant colony optimization and back 社,2010) propagation improved by Markov chain.J Yangte River Sci Res [16]Hardle W K,Simar L.Applied Multivariate Statistical Analysis msL2019,36(7):48 Upper Saddle River:Springer-Verlag Berlin Heidelberg,2015 (董丹丹,祖安君,孙雪莲.基于GACO-BP-MC的大坝变形监控 [17]Sun WZ,Yin X D,Li S J.A new navigation data fusion method 模型.长江科学院院报,2019,36(7):48) based on entropy coefficient algorithm for underwater vehicles. [5]Wei B W,Peng S J,Xu ZK,et al.The GA-BP prediction model Geomat Inf Sci Wuhan Univ,2018,43(10):1465 considering chaos effect of dam displacement residual.Sci Sin (孙文舟,殷晓冬,李树军.基于熵权重的水下载体导航信息融 Technol,.2015,45(5):541 合方法.武汉大学学报(信息科学版),2018,43(10):1465) [6]Hu D X,Qu X D,Yang J,et al.A safety monitoring model of dam [18]Liu H X,Yuan Y M,Zhou P,et al.Normal cloud model for deformation based on M-ELM.Ady Sci Technol Water Resour, condition evaluation of wind turbines based on fusion theory.Acta 2019,39(3:75 Energ Sol Sin,2018,39(10):2891 (胡德秀,屈旭东,杨杰,等.基于M-ELM的大坝变形安全监控模 (刘华新,苑一鸣,周沛,等.基于融合理论的风电机组状态评价 型.水利水电科技进展,2019,39(3):75) 正态云模型.太阳能学报,2018,39(10):2891) [7]Dai B,Gu C S,Zhao E F,et al.Statistical model optimized random [19]Du J,Sun M Y.Hierarchical assessment method of transformer forest regression model for concrete dam deformation monitoring. condition based on weight-varying grey cloud model.Trans China Struct Control Health Monitor,2018,25(6):e2170 Electrotech Soc,2020,35(20):4306 [8] Kang F,Liu J,Li J J,et al.Concrete dam deformation prediction (杜江,孙铭阳.基于变权灰云模型的变压器状态层次评估方法, model for health monitoring based on extreme learning machine. 电工技术学报,2020,35(20):4306) Struct Control Health Monitor,2017,24(10):e1997 [20]Zhang MY,Wang SX.Sun ZZ,et al.Comprehensive evaluation [9]Gamse S.Oberguggenberger M.Assessment of long-term of landslide risks of oil and gas pipelines based on cloud theory. coordinate time series using hydrostatic-season-time model for Chin J Eng,2018,40(4:427 rock-fill embankment dam.Struct Control Health Monitor,2017, (张满银,王生新,孙志忠,等.基于云理论的油气管道滑坡危险 24(1):e1859 性综合评价.工程科学学报,2018,40(4):427) [10]Gamse S,Henriques M J,Oberguggenberger M,et al.Analysis of [21]Li L B,Guo X F,Fu J N,et al.Evaluation approach of passenger periodicities in long-term displacement time series in concrete satisfaction for urban rail transit based on cloud model.J Tongji dams.Struct Control Health Monitor,2020,27(3):e2477 Univ Nat Sci,.2019,47(3):378 [11]Liu X.WuZ R.Yang Y,et al.Information fusion diagnosis and (李林波,郭晓凡,傅佳楠,等.基于云模型的城市轨道交通 early-warning method for monitoring the long-term service safety 乘客满意度评价.同济大学学报(自然科学版),2019,47(3): of high dams.J Zhejiang Univ Sci A Appl Phys Eng,2012,13(9) 378) 687 [22]Li S S,Cui T J,Ma Y D,et al.Cloud similarity based on the [12]He J P,Ma C B,Shi Y Q.Multi-effect-quantity fusion model of envelope and its application to the safety assessment.J Saf high arch dam based on improved D-S evidence theory.Geomar Environ,2017,17(4):1267 Inf Sci Wuhan Univ,2012,37(12):1397 (李莎莎,崔铁军,马云东,等.基于包络线的云相似度及其在安 (何金平,马传彬,施玉群.高拱坝多效应量改进型D-$证据 全评价中的应用.安全与环境学报,2017,17(4):1267) 理论融合模型.武汉大学学报(信息科学版),2012,37(12): [23]Wang J,Zhu J J,Liu X D.An integrated similarity measure 1397) method for normal cloud model based on shape and distance. [13]Su H Z,Wen Z P,Sun X R,et al.Multisource information fusion- System Eng Theory Pract,2017,37(3):742(顾冲时, 苏怀智, 王少伟. 高混凝土坝长期变形特性计算模型 及监控方法研究进展. 水力发电学报, 2016, 35(5):1) Li M C, Ren Q B, Kong R, et al. Dynamic modeling and prediction analysis  of  dam  deformation  under  multidimensional  complex relevance. J Hydraul Eng, 2019, 50(6): 687 (李明超, 任秋兵, 孔锐, 等. 多维复杂关联因素下的大坝变形动 态建模与预测分析. 水利学报, 2019, 50(6):687) [3] Dong  D  D,  Zu  A  J,  Sun  X  L.  Model  of  dam  deformation monitoring  based  on  genetic  ant  colony  optimization  and  back propagation improved by Markov chain. J Yangtze River Sci Res Inst, 2019, 36(7): 48 (董丹丹, 祖安君, 孙雪莲. 基于GACO-BP-MC的大坝变形监控 模型. 长江科学院院报, 2019, 36(7):48) [4] Wei B W, Peng S J, Xu Z K, et al. The GA-BP prediction model considering  chaos  effect  of  dam  displacement  residual. Sci Sin Technol, 2015, 45(5): 541 [5] Hu D X, Qu X D, Yang J, et al. A safety monitoring model of dam deformation  based  on  M-ELM. Adv Sci Technol Water Resour, 2019, 39(3): 75 (胡德秀, 屈旭东, 杨杰, 等. 基于M-ELM的大坝变形安全监控模 型. 水利水电科技进展, 2019, 39(3):75) [6] Dai B, Gu C S, Zhao E F, et al. Statistical model optimized random forest regression model for concrete dam deformation monitoring. Struct Control Health Monitor, 2018, 25(6): e2170 [7] Kang F, Liu J, Li J J, et al. Concrete dam deformation prediction model  for  health  monitoring  based  on  extreme  learning  machine. Struct Control Health Monitor, 2017, 24(10): e1997 [8] Gamse  S,  Oberguggenberger  M.  Assessment  of  long-term coordinate  time  series  using  hydrostatic-season-time  model  for rock-fill embankment dam. Struct Control Health Monitor, 2017, 24(1): e1859 [9] Gamse S, Henriques M J, Oberguggenberger M, et al. Analysis of periodicities  in  long-term  displacement  time  series  in  concrete dams. Struct Control Health Monitor, 2020, 27(3): e2477 [10] Liu X, Wu Z R, Yang Y, et al. Information fusion diagnosis and early-warning method for monitoring the long-term service safety of high dams. J Zhejiang Univ Sci A Appl Phys Eng, 2012, 13(9): 687 [11] He J P, Ma C B, Shi Y Q. Multi-effect-quantity fusion model of high  arch  dam  based  on  improved  D-S  evidence  theory. Geomat Inf Sci Wuhan Univ, 2012, 37(12): 1397 (何金平, 马传彬, 施玉群. 高拱坝多效应量改进型D-S证据 理论融合模型. 武汉大学学报(信息科学版),  2012,  37(12): 1397) [12] [13] Su H Z, Wen Z P, Sun X R, et al. Multisource information fusion￾based approach diagnosing structural behavior of dam engineering. Struct Control Health Monitor, 2018, 25(2): e2073 Yu  H,  Wu  Z  R,  Bao  T  F,  et  al.  Multivariate  analysis  in  dam monitoring  data  with  PCA. Sci China Technol Sci,  2010,  53(4): 1088 [14] He  J  P. Theory and Application of Dam Safety Monitoring. Beijing: China Water & Power Press, 2010 ( 何金平. 大坝安全监测理论与应用. 北京: 中国水利水电出版 社, 2010) [15] Härdle  W  K,  Simar  L. Applied Multivariate Statistical Analysis. Upper Saddle River: Springer-Verlag Berlin Heidelberg, 2015 [16] Sun W Z, Yin X D, Li S J. A new navigation data fusion method based  on  entropy  coefficient  algorithm  for  underwater  vehicles. Geomat Inf Sci Wuhan Univ, 2018, 43(10): 1465 (孙文舟, 殷晓冬, 李树军. 基于熵权重的水下载体导航信息融 合方法. 武汉大学学报(信息科学版), 2018, 43(10):1465) [17] Liu  H  X,  Yuan  Y  M,  Zhou  P,  et  al.  Normal  cloud  model  for condition evaluation of wind turbines based on fusion theory. Acta Energ Sol Sin, 2018, 39(10): 2891 (刘华新, 苑一鸣, 周沛, 等. 基于融合理论的风电机组状态评价 正态云模型. 太阳能学报, 2018, 39(10):2891) [18] Du  J,  Sun  M  Y.  Hierarchical  assessment  method  of  transformer condition based on weight-varying grey cloud model. Trans China Electrotech Soc, 2020, 35(20): 4306 (杜江, 孙铭阳. 基于变权灰云模型的变压器状态层次评估方法. 电工技术学报, 2020, 35(20):4306) [19] Zhang M Y, Wang S X, Sun Z Z, et al. Comprehensive evaluation of landslide risks of oil and gas pipelines based on cloud theory. Chin J Eng, 2018, 40(4): 427 (张满银, 王生新, 孙志忠, 等. 基于云理论的油气管道滑坡危险 性综合评价. 工程科学学报, 2018, 40(4):427) [20] Li L B, Guo X F, Fu J N, et al. Evaluation approach of passenger satisfaction  for  urban  rail  transit  based  on  cloud  model. J Tongji Univ Nat Sci, 2019, 47(3): 378 (李林波, 郭晓凡, 傅佳楠, 等. 基于云模型的城市轨道交通 乘客满意度评价. 同济大学学报(自然科学版), 2019, 47(3): 378) [21] Li  S  S,  Cui  T  J,  Ma  Y  D,  et  al.  Cloud  similarity  based  on  the envelope  and  its  application  to  the  safety  assessment. J Saf Environ, 2017, 17(4): 1267 (李莎莎, 崔铁军, 马云东, 等. 基于包络线的云相似度及其在安 全评价中的应用. 安全与环境学报, 2017, 17(4):1267) [22] Wang  J,  Zhu  J  J,  Liu  X  D.  An  integrated  similarity  measure method  for  normal  cloud  model  based  on  shape  and  distance. System Eng Theory Pract, 2017, 37(3): 742 [23] · 472 · 工程科学学报,第 44 卷,第 3 期
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