全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

聚类分析在隧道开挖变形速率中的应用

, PP. 97-102

Keywords: 隧道工程,主因素分析,层次聚类,变形速率,最小二乘法

Full-Text   Cite this paper   Add to My Lib

Abstract:

围岩变形是隧道开挖过程中很重要的一项施工监控的指标,将聚类方法引入到具体的隧道开挖过程中,挖掘出影响变形速率的主导因素,从而为隧道施工过程中变形的控制决策提供参考。利用层次聚类对监测点进行分类,采用最小二乘法曲线拟合对相关数据进行预处理,再采用层次聚类法确定影响变形的主因素,并最终得出结论开挖面距离与隧道埋深是决定变形的两个主要因素,对围岩的变形产生重要影响,得出的结果将反馈到具体的隧道开挖过程中指导后续施工。

References

[1]  AGRAWAL R,MANNILA H,SRIKANT R.Fast Discovery of Association Rules:Advances in Knowledge Discovery and Data Mining[M].California:MIT Press,1996:307-328.
[2]  杨新苗.城市公交发展技术保障体系关键技术研究[D].南京:东南大学,2000.YANG Xinmiao.Research on the Key Technology for Technical Guarantee System of Urban Transit Development[D].Nanjing:Nanjing Southeast University,2000.
[3]  许宏科,揣锦华.公路隧道交通流的数据挖掘[J].长安大学学报:自然科学版,2005(4):27-29.XU Hongke,CHUAI Jinhua.Data Mining of Traffic Flow in Road Tunnel[J].Journal of Chang'an University,2005(4):27-29.
[4]  刘长祥,金秀丽.模糊聚类分析在隧道围岩稳定性预测中的应用[J].山西建筑,2007(7):16-18.LIU Changxiang,JIN Xiuli.The Application of Fuzzy Cluster Method for the Forecast of the Rock Stability[J].Shanxi Architecture,2007(7):16-18.
[5]  MARGARET H,DUNHAM.数据挖掘教程[M].郭崇慧,田凤占,靳晓明,等译.北京:清华大学出版社,2005.MARGARET H,DUNHAM.Data Mining Tutorial[M].GUO Chonghui,TIAN Fengzhan,JIN Xiaoming,et al translated.Beijing:Tsinghna University Press,2005.
[6]  DZEROSKI S.Multi-relational Data Mining:An Introduction[J].ACM SIGKDD Explorations Newsletter,2003,5(1):1-16.
[7]  JIAWEI H,MICHELINE K.数据挖掘概念与技术[M].范明,孟小峰,译.北京:机械工业出版社,2004.JIAWEI H,MICHELINE K.Data Mining Concepts and Techniques[M].FAN Ming,MENG Xianfeng,translated.Beijing:China Machine Press,2004.
[8]  中华人民共和国交通部.JTJ042-1994公路隧道施工技术规范[S].北京:人民交通出版社,1995.P.R.China.Ministry of Communications.JTJ042-1994 Techcical Speci6catiorIs for Constrction of Highway Tunnel[S].Beijing:China Communications Press,1995.
[9]  RAEDT L D,KERSTJNG K.Probabilistic Logic Learning[J].ACM-SIGKDD Explorations Newsletter,2003,5(1):31-48.
[10]  廉旭刚,戴华阳.隧道围岩变形监测及变形规律分析[J].矿山测量,2008(1):13-15.LIAN Xugang,DAI Huayang.Deformation Monitoring of Adjoining Rock around the Tunnel and Analysis of the Deformation Laws[J].Mine Surveying,2008(1):13-15.
[11]  彭远芳,董红生.电力系统中基于聚类分析的主导因素挖掘方法[J].中国电力,2006(12):10-14.PENG Yuanfang,DONG Hongsheng.Dominant Factors Mining in Power System Based on Clustering Analysis[J].Electric Power,2006(12):10-14.
[12]  JOHNSON R A.Applied Multivariate Statistical Analysis[M].New Jersey:Prentice-Hall Inc.,1999:312-324.
[13]  SRIKANT R,AGRAWAL R.Mining Quantitative Association Rules in Large Relational Table[C]//Proceedings of the ACM SIGMOD Conference on Management of Data.New York:ACM Press,1996:67-71.
[14]  JAIN A K,DUBES R C.Algorithms for Clustering Data[M].Englewood Cliffs,New Jersey:Prentice Hall,1998.
[15]  GANTER B,WILLE R.Formal Concept Analysis:Mathematical Foundations[M].Berlin:Springer,1999.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133