Advertisement

Statistical Model’s Application in the Gross Error Recognition of Deformation Monitoring Data of Dam

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 287)

Abstract

Dam deformation is affected by many factors, and its abnormal observed value is not surely the gross error. In order to effectively identify the gross error in the safety monitoring data of dam, the statistical model used in safety monitoring of dam and its bases is introduced into the gross error recognition of monitoring data on the basis of analyzing the statistical model theory and the reason that the gross errors are generated. First of all, the data containing the gross error are used to establish the statistical model, and then according to the residual error between the fitting results of statistical model and the real measured value, the quartile method is used to set threshold and recognize the gross errors. For some concrete gravity dam, after the gross errors are added into the monitoring data of tension wire on the top of dam, the actual situation is simulated. Through this method, the added gross errors are completely recognized.

Keywords

Dam safety monitoring Statistical model Gross error recognition Quartile method 

References

  1. 1.
    Cong P (2005) The probabilistic identification method of abnormal value of dam monitoring data. Hydroelectric Energy Sci 23(4):32–34Google Scholar
  2. 2.
    Fei Y (2010) Error theory and data processing. Mechanical Industrial Press, BeijingGoogle Scholar
  3. 3.
    Wu ZR (2003) The safety monitoring theory of hydraulic structure and its application. Higher Education Press, BeijingGoogle Scholar
  4. 4.
    Gu C, Li Y, Song J (2010) The research on the safety monitoring model of roller compacted concrete dam deformation. Chin J Comput Mech 27(2):286–290Google Scholar
  5. 5.
    Zhang Y, Zhao Y (2004) The detection of outlier data. Adv Astron 22(1):1–9MATHGoogle Scholar
  6. 6.
    Ji J, Gu C (2009) The application of mathematical morphology filtering in the gross error of dam safety monitoring data. J Wuhan Univ Inf Sci Ed 34(9):1126–1129Google Scholar
  7. 7.
    Sarjakoski T (1982) Artificial intelligence in photogrammetry. Photogrammetria Amsterdam 52(5):245–270Google Scholar
  8. 8.
    Chengxiang Y, Xiwei Z, Fengpeng Z, Changyu J, Jianpo L, Yifei Z (2012) Estimation of amplification effect of mining-induced blast vibration on surrounding structures using a hybrid GA-SVM. Disaster Adv 5(4):1113–1118Google Scholar
  9. 9.
    Broomhead DS, King GP (1986) Broomhead qualitative dynamics from experimental data. Physica D 20(2–3):217–236CrossRefMATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.State Key Laboratory of Hydrology-Water Resources and Hydraulic EngineeringHohai UniversityNanjingChina
  2. 2.College of Water-conservancy and HydropowerHohai UniversityNanjingChina
  3. 3.National Engineering Research Center of Water Resources Efficient Utilization and Engineering SafetyHohai UniversityNanjingChina
  4. 4.School of Hydraulic EngineeringDalian University of TechnologyDalianChina

Personalised recommendations