Citation: | JIANG Zhen-xiang, CHEN Hui, CHEN Bai-quan. Evaluation model of overall dam behavior based on cloud theory[J]. Chinese Journal of Engineering, 2022, 44(3): 464-473. doi: 10.13374/j.issn2095-9389.2020.10.15.001 |
[1] |
朱伯芳. 中國是世界拱壩大國也是世界拱壩強國——在《特高拱壩建設總結及安全運行管理研究》會議上的發言. 水利水電技術, 2017, 48(2):1
Zhu B F. China is the largest and strongest country of concrete arch dams in the world. Water Resour Hydropower Eng, 2017, 48(2): 1
|
[2] |
顧沖時, 蘇懷智, 王少偉. 高混凝土壩長期變形特性計算模型及監控方法研究進展. 水力發電學報, 2016, 35(5):1 doi: 10.11660/slfdxb.20160501
Gu C S, Su H Z, Wang S W. Advances in calculation models and monitoring methods for long-term deformation behavior of concrete dams. J Hydroelectr Eng, 2016, 35(5): 1 doi: 10.11660/slfdxb.20160501
|
[3] |
李明超, 任秋兵, 孔銳, 等. 多維復雜關聯因素下的大壩變形動態建模與預測分析. 水利學報, 2019, 50(6):687
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
|
[4] |
董丹丹, 祖安君, 孫雪蓮. 基于GACO-BP-MC的大壩變形監控模型. 長江科學院院報, 2019, 36(7):48 doi: 10.11988/ckyyb.20171438
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 doi: 10.11988/ckyyb.20171438
|
[5] |
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 doi: 10.1360/N092014-00181
|
[6] |
胡德秀, 屈旭東, 楊杰, 等. 基于M-ELM的大壩變形安全監控模型. 水利水電科技進展, 2019, 39(3):75 doi: 10.3880/j.issn.1006-7647.2019.03.013
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 doi: 10.3880/j.issn.1006-7647.2019.03.013
|
[7] |
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 doi: 10.1002/stc.2170
|
[8] |
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 doi: 10.1002/stc.1997
|
[9] |
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 doi: 10.1002/stc.1859
|
[10] |
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
|
[11] |
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 doi: 10.1631/jzus.A1200122
|
[12] |
何金平, 馬傳彬, 施玉群. 高拱壩多效應量改進型D-S證據理論融合模型. 武漢大學學報(信息科學版), 2012, 37(12):1397
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
|
[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 doi: 10.1002/stc.2073
|
[14] |
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 doi: 10.1007/s11431-010-0060-1
|
[15] |
何金平. 大壩安全監測理論與應用. 北京: 中國水利水電出版社, 2010
He J P. Theory and Application of Dam Safety Monitoring. Beijing: China Water & Power Press, 2010
|
[16] |
H?rdle W K, Simar L. Applied Multivariate Statistical Analysis. Upper Saddle River: Springer-Verlag Berlin Heidelberg, 2015
|
[17] |
孫文舟, 殷曉冬, 李樹軍. 基于熵權重的水下載體導航信息融合方法. 武漢大學學報(信息科學版), 2018, 43(10):1465
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
|
[18] |
劉華新, 苑一鳴, 周沛, 等. 基于融合理論的風電機組狀態評價正態云模型. 太陽能學報, 2018, 39(10):2891
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
|
[19] |
杜江, 孫銘陽. 基于變權灰云模型的變壓器狀態層次評估方法. 電工技術學報, 2020, 35(20):4306
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
|
[20] |
張滿銀, 王生新, 孫志忠, 等. 基于云理論的油氣管道滑坡危險性綜合評價. 工程科學學報, 2018, 40(4):427
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
|
[21] |
李林波, 郭曉凡, 傅佳楠, 等. 基于云模型的城市軌道交通乘客滿意度評價. 同濟大學學報(自然科學版), 2019, 47(3):378
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
|
[22] |
李莎莎, 崔鐵軍, 馬云東, 等. 基于包絡線的云相似度及其在安全評價中的應用. 安全與環境學報, 2017, 17(4):1267
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
|
[23] |
汪軍, 朱建軍, 劉小弟. 兼顧形狀-距離的正態云模型綜合相似度測算. 系統工程理論與實踐, 2017, 37(3):742 doi: 10.12011/1000-6788(2017)03-0742-10
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 doi: 10.12011/1000-6788(2017)03-0742-10
|
[24] |
李海林, 郭崇慧, 邱望仁. 正態云模型相似度計算方法. 電子學報, 2011, 39(11):2561
Li H L, Guo C H, Qiu W R. Similarity measurement between normal cloud models. Acta Electron Sin, 2011, 39(11): 2561
|
[25] |
Kao C Y, Loh C H. Monitoring of long-term static deformation data of Fei-Tsui arch dam using artificial neural network-based approaches. Struct Control Health Monitor, 2013, 20(3): 282 doi: 10.1002/stc.492
|
[26] |
李天倫, 何安瑞, 邵健, 等. 基于Copula函數的熱軋支持輥健康狀態預測模型. 工程科學學報, 2020, 42(6):787
Li T L, He A N, Shao J, et al. Copula-based model for hot-rolling back-up roll health prediction. Chin J Eng, 2020, 42(6): 787
|
[27] |
傅立偉, 武森. 基于屬性值集中度的分類數據聚類有效性內部評價指標. 工程科學學報, 2019, 41(5):682
Fu L W, Wu S. A new internal clustering validation index for categorical data based on concentration of attribute values. Chin J Eng, 2019, 41(5): 682
|
[28] |
徐波, 李占超, 黃志洪, 等. 混凝土壩裂縫轉異診斷的云模型法. 中國科學(技術科學), 2015, 45(11):1218 doi: 10.1360/N092014-00195
Xu B, Li Z C, Huang Z H, et al. Cloud model diagnosis method of concrete dam crack behavior abnormality. Sci Sin Technol, 2015, 45(11): 1218 doi: 10.1360/N092014-00195
|