考虑黏弹性滞后效应的拱坝位移监控组合模型

Combinatorial Monitoring Model for Displacement of Arch Dams Considering Viscoelastic Hysteretic Effect

位移监控模型需要对拱坝变形性态兼具良好的解释和预测能力。水压-滞后-周期性温度-时效四因子HHST(Hydraulic,Hysteretic,Seasonal and Time)模型能够合理地解释锦屏一级拱坝的黏弹性滞后变形性态。为进一步提升该模型的预测精度,使用支持向量机(SVM)建立有限元计算所得拱坝黏弹性滞后位移与其因果因子之间的隐式关系,再将其融入到HHST模型中,进而基于多元线性回归建立拱坝位移的组合监控模型。以锦屏一级拱坝为例,减少输入因子数的组合模型的预测精度明显高于直接以HHST模型中18个因子作为输入的单一模型;SVM对滞后水压位移分量的预测精度明显高于基于约束最小二乘法的线性回归模型,采用2种滞后水压分量所建组合模型对拱坝变形性态具有相近的解释能力,而采用SVM滞后水压分量建立的组合模型可有效地提高拱坝位移的预测精度,多测点均方误差(MSE)平均降低21.67%,决定系数R2整体提高0.07%。

A displacement monitoring model should well interpret and predict the deformation behavior of arch dam.HHST model could explain the viscoelastic hysteretic deformation behavior of Jinping-I arch dam.To further improve the prediction accuracy of the HHST model,the nonlinear relationship between the finite element method(FEM)-calculated viscoelastic hysteretic displacement of arch dam and its causal factors is modeled by the support vector machine(SVM)and is used as a whole variable in the HHST model.In subsequence,a combinatorial monitoring model is established for the displacement of arch dam based on multiple linear regression(MLR).Case study of the Jinping-I arch dam shows that the prediction accuracy of the combinatorial monitoring model,which has a reduced number of input factors,is significantly higher than that of simple models directly established with all the 18 causal factors of the HHST model.SVM has a better prediction accuracy for the hysteretic hydraulic displacement than that of constrained least square method-based linear regression model.The two combined monitoring models,respectively using the SVM and linear regression-based hysteretic hydraulic displacement component,have similar interpretation ability for the measured deformation behavior of arch dams,while the former can effectively improve the prediction accuracy of dam displacement,with the average mean square error(MSE)of multiple monitoring points dropping by 21.67%and the average determination coefficient R2 rising by 0.07%.