基于逐步回归分析—马尔可夫链模型的大坝变形预测

Deformation Prediction of Dam Based on Stepwise Regression Analysis-Markov Chain Model

针对预测大坝变形准确性难度较大的问题,综合逐步回归分析和马尔可夫链的优点,采用逐步回归分析法对大坝原型观测资料进行分析,得到回归模型,并判别回归方程的有效性和精度,同时利用马尔可夫链确定位移时序的状态转移概率矩阵,通过划分残差状态、修正实测值与逐步回归模型拟合值的绝对误差与相对误差,建立了大坝变形预测的逐步回归分析—马尔可夫链预测模型(SRA-MC)。实例应用结果表明,模型的拟合值与实测值吻合良好,预测效果好,可见逐步回归分析—马尔可夫链模型在进行大坝变形预测时具有有效性,可应用于大坝变形预测分析及大坝安全监控预警中。

The prediction accuracy of dam deformation is a difficult problem. In this paper, the advantages of stepwise regression analysis and Markov chain are integrated. Firstly, the stepwise regression model is used to analyze the dam observation data and get the regression equation. And then, the validity and accuracy of the regression equation is determined. At the same time, Markov chain was applied to determine the state transition probability matrix of displacement series. By classifying the state of residual error and modifying absolute and relative errors between measured values and fitting values, a new dam deformation prediction model named as stepwise regression analysis-Markov chain model is proposed. The example of application results show that the dam deformation measured values are in good agreement with the predicted results, which indicates that the stepwise regression analysis-Markov chain model is valid and it can be used for dam deformation prediction analysis and early-warning monitoring.