The Diagnostic Method of Concrete Dam Monitoring Information Valid Interval


Article Preview

On the basis of the analysis of the displacement of concrete dam and its related influential factors, based on the evolvement of nonlinear dynamics of concrete dam, it can effectively identify the mutations position of measured value and the attribute interval of dynamical system applied with the wavelet analysis, dynamic structural mutation theory and other numerical analysis methods. When detecting after separating structural mutation sequence, it can finally get the relative stable displacement time series of dynamical structure, so it can realize the diagnostic separation of the monitoring information effective interval. At the end of the paper, through applying a certain concrete arch dam, it is proved that the proposed method of concrete dam mutations diagnosis of is of great significance for the real-time monitoring of the workability state of a dam.



Edited by:

Chunliang Zhang and Paul P. Lin




D. Qin et al., "The Diagnostic Method of Concrete Dam Monitoring Information Valid Interval", Applied Mechanics and Materials, Vols. 226-228, pp. 2072-2077, 2012

Online since:

November 2012




[1] WU Zhongru. Safety Monitoring Theory & Its Application of Hydraulic Structures. Beijing. Higher Education Press. 2003. (In Chinese).

[2] XU Hongzhong. Nonlinear Analysis Model for Safety Monitoring of Large Dynamic System. Doctoral Dissertation. Nanjing: Hohai University, 2001(In Chinese).

[3] Gribkov D, Gribkova V. Learning dynamics from nonstationary time series: Analysis of Electroencephalograms. Phys Rev E, 2000, 61(6): 6538–6545.


[4] Fraser A M. Information and entropy in strange attractors[J]. IEEE trans inform theory, 1989, 35(2): 245-262.


[5] Wales D J. calculating the rate of information from chaotic time series by forcasting[J]. Nature, 1991, 350(6318): 485-488.


[6] CHENG Zhengxing. Wavelet Analysis Algorithm and Application. Xi'an Jiaotong University Press, 1988. (In Chinese).

[7] Mallat S. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans on PAMI, 1989, 11(7): 674–693.

[8] Manuca R, Savit R. Stationarity and nonstationarity in time series analysis. Phys D, 1996, 99: 134–161.


[9] LI Chungui, PEI Liuqing. A Method for Distinguishing Dynamical Species in Chaotic Time Series. Acta Physica Sinica., 2003, 52(9): 2114-2120(In Chinese).

[10] Kantz H. Quantifying the closeness of fractal measures. Phys Rev E, 1994, 49(6): 5091–5097.