期刊文献+

Real-Time Spreading Thickness Monitoring of High-core Rockfill Dam Based on K-nearest Neighbor Algorithm 被引量:4

Real-Time Spreading Thickness Monitoring of High-core Rockfill Dam Based on K-nearest Neighbor Algorithm
下载PDF
导出
摘要 During the storehouse surface rolling construction of a core rockfilldam, the spreading thickness of dam face is an important factor that affects the construction quality of the dam storehouse' rolling surface and the overallquality of the entire dam. Currently, the method used to monitor and controlspreading thickness during the dam construction process is artificialsampling check after spreading, which makes it difficult to monitor the entire dam storehouse surface. In this paper, we present an in-depth study based on real-time monitoring and controltheory of storehouse surface rolling construction and obtain the rolling compaction thickness by analyzing the construction track of the rolling machine. Comparatively, the traditionalmethod can only analyze the rolling thickness of the dam storehouse surface after it has been compacted and cannot determine the thickness of the dam storehouse surface in realtime. To solve these problems, our system monitors the construction progress of the leveling machine and employs a real-time spreading thickness monitoring modelbased on the K-nearest neighbor algorithm. Taking the LHK core rockfilldam in Southwest China as an example, we performed real-time monitoring for the spreading thickness and conducted real-time interactive queries regarding the spreading thickness. This approach provides a new method for controlling the spreading thickness of the core rockfilldam storehouse surface. During the storehouse surface rolling construction of a core rockfilldam, the spreading thickness of dam face is an important factor that affects the construction quality of the dam storehouse' rolling surface and the overallquality of the entire dam. Currently, the method used to monitor and controlspreading thickness during the dam construction process is artificialsampling check after spreading, which makes it difficult to monitor the entire dam storehouse surface. In this paper, we present an in-depth study based on real-time monitoring and controltheory of storehouse surface rolling construction and obtain the rolling compaction thickness by analyzing the construction track of the rolling machine. Comparatively, the traditionalmethod can only analyze the rolling thickness of the dam storehouse surface after it has been compacted and cannot determine the thickness of the dam storehouse surface in realtime. To solve these problems, our system monitors the construction progress of the leveling machine and employs a real-time spreading thickness monitoring modelbased on the K-nearest neighbor algorithm. Taking the LHK core rockfilldam in Southwest China as an example, we performed real-time monitoring for the spreading thickness and conducted real-time interactive queries regarding the spreading thickness. This approach provides a new method for controlling the spreading thickness of the core rockfilldam storehouse surface.
出处 《Transactions of Tianjin University》 EI CAS 2018年第3期282-289,共8页 天津大学学报(英文版)
基金 supported by the Innovative Research Groups of National Natural Science Foundation of China(No. 51621092) National Basic Research Program of China ("973" Program, No. 2013CB035904) National Natural Science Foundation of China (No. 51439005)
关键词 Core rockfill dam Dam storehouse surface construction Spreading thickness K-nearest neighbor algorithm Real-time monitor 实时传播 厚度 水坝 算法 邻居 表面 构造 即时
  • 相关文献

参考文献4

二级参考文献42

  • 1ZHONG DengHua,CUI Bo,LIU DongHai,TONG DaWei.Theoretical research on construction quality real-time monitoring and system integration of core rockfill dam[J].Science China(Technological Sciences),2009,52(11):3406-3412. 被引量:61
  • 2YI TingHua1,2, LI HongNan1 & GU Ming2 1 Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116023, China,2 State Key Laboratory for Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China.Recent research and applications of GPS based technology for bridge health monitoring[J].Science China(Technological Sciences),2010,53(10):2597-2610. 被引量:18
  • 3王柏乐,刘瑛珍,吴鹤鹤.中国土石坝工程建设新进展[J].水力发电,2005,31(1):63-65. 被引量:34
  • 4黄声享,刘经南,吴晓铭.GPS实时监控系统及其在堆石坝施工中的初步应用[J].武汉大学学报(信息科学版),2005,30(9):813-816. 被引量:50
  • 5Kobbelt L P, Botsch M, Schwanecke U, et al. Feature sensitive surface extraction from volume data [A]. In:Computer Graphics Proceedings, Annual Conference Series, ACM, SIGGRAPH, Los Angeles, CA, 2001. 57~66
  • 6Goodsell. G. On finding p-th nearest neighbors of scattered points in two dimensions for small p [J]. Computer Aided Geometric Design, 2000,17(4): 387~ 392
  • 7Dickerson M T, Drysdale R L S, Sack J R. Simple algorithms for enumerating interpoint distances and finding k nearest neighbors [J ]. International Journal of Computational Geometry and Applications, 1992, 2(3): 221~239
  • 8Piegl L A, Tiller W. Algorithm for finding all k nearest neighbors [J]. Computer-Aided Design, 2002, 34(2): 167~172
  • 9Attamimi, M., Mizutani, A., Nalmura, T., et al., 2010. Real-time 3D visuM sensor for robust object recognition. IEEE/RSJ Int. Conf. on Intelligent Robots and Sys- tems, p.4560-4565. [doi:10.1109/IROS.2010.5650455].
  • 10Buehler, C., Bosse, M., McMillan, L., et al., 2001. Unstruc- tured lumigraph rendering. Proc. 28th Annual Conf.on Computer Graphics and Interactive Techniques, p.425-432. [doi: 10.1145/383259.383309].

共引文献142

同被引文献36

引证文献4

二级引证文献23

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部