Study On Analysis Methods For Dam Safety Monitoring Data

Analysis theory and Methods for dam safety monitoring data, playing a major role in guaranteeing the dam safety, have been made considerable progress. However, there are still many problems and and deficiencies in the data analysis. In this paper, research theory and analytical methods in the other fields are introduced and applied to the analysis of dam monitoring data such as the analysis of deformation monitoring data of concrete dam. The main purpose of study in this paper is to solve some problems and to correct some deficiencies of the analysis of dam monitoring data in the existing, for improving the monitoring data analysis precision of the model and achieving more effective and rational assessment of the state of the dam, in order to meet the needs of practical engineering applications.In order to avoid possible suprious regression in regression model, in this paper, cointegration theorom is used to test whether a set of data of dam monitoring variables are stationary time series. The cointegrated series can be represented by error correction models to describe long run equilibrium and short run unequilibrium among them, in order to improve the model fittness and predictions capacity.In order to evaluate the security state and the structural change trend of the dam, a kind of safety monitoring index is introduced, according to the distance to the unit circle of roots which are calculated from the characteristic polynomial of stationary autoregression system reflects the properties of stationary system.As the higher-order statistics of time series which contains more information than a second-order statistics can better reflect the properties of system, this paper describes the modern spectral estimation and bispectrum estimation theory, principles and methods, the analysis result of a damage experiment of two reinfoced concrete beams indicates bispectrum can effectively reflect the change of structure status, and try for the analysis of dam monitoring data to assess the structural state of the dam.Considering the importance of time-effect displacement in deformation monitoring data analysis, suppose that the dam system is a time invariable system, the time-effect variable is selected as state variable to discribe the structural properties of dam body. The parameters of state space model arc estimated by using the EM algorithm. The example analysis result shows that the state space model has good precision of fitness and forecastion, and time-effect variable can effectively extract from the monitoring data of dam used to evalute the dam status.Multicollinearity of independent variables and random noise tend to result in overfitting of the regression model, making the model fitting accuracy is high, but the predictive ability is poor, so that the regression model can not be effectively applied to forecast for dam safety monitoring. In this paper, the group method of data handling(GMDH) of self-organizing data mining is used to make model in order to enhance the robustness of the model and to improve the model's predictive ability, example analysis result validates the effectiveness of the method.