Study On Dam Safety Monitoring Statistics Models

In order to meet the requirement of dam safety monitoring, according to previous statistic method and focus on the defects of least square regression statistic theory, the models for dam safety monitoring were put forward in the dissertation based on the partial least square square regression, Kalman filtering, BP and RBF neural network. The performances of the models were compared in detail through the engineering practice. The study was not only of practical importance for hydraulic projects, but also of vital significance for promoting dam safety control level in our country. The contents and major results are as follows:(1) The problem of least square regression method in formulating model structure was analyzed, based on which it was pointed out that the multi-relativity among factors of least squares regression model was the key matter in causing structure instability and unclear explanation. Therefore, based on regression theory and aiming at statistic model formulating, the statistic model of partial least square regression was presented the first time. It is indicated through case study that the model can effectively overcome the serious multi-relativity among factors; consequently it can make the structure of statistic model stable and enhance the result explanation. It was shown by comparing of modeling results and project data that the model was a powerful tool for formulating basic structure under the condition of multi-relativity among factors. Therefore, the innovative results of the study are stable model structure formulated on the basis of partial least square method and using the regression coefficient as a variable in evaluation of dam safety.(2) Based on Kalman filter theory , the Kalman filtering statistic model was presented for formulating model structure. This model combined Kalman filtering theory with statistic model of dam safety monitoring, which was the ideology of the study. It was shown by study that this model provided a recursion solution for estimation with the Kalman filter, which can simplify the modeling process as on-line refreshing state conveniently through the previous time state value and present measure value, so it was a new efficient on-line modeling method. Because the Kalman filter belonged to the minimum variance estimation, precision of the Kalman filtering regression statistic model was higher than that of partial least square and least square regression statistic model. It was shown by comparison between actual project data and modeling results that...