考虑不确定性概念的高面板堆石坝动力参数反演分析

Inversion Analysis of Dynamic Parameters of High Panel Rockfill Dams Considering Concept of Uncertainty

针对目前动本构模型参数反演方法大多未考虑参数不确定性的问题,将自适应云变换算法(AGCT)与RBF神经网络(RBFNN)相结合,构建了自适应云神经网络参数反演模型(AGCTNN),将不确定性概念转换为定量数值,较好地考虑了大坝系统间的随机性与模糊性对动力参数反演的影响。对比分析AGCT与K-Means、SOM、DBSCAN三种传统聚类算法,验证了算法的优越性与可行性,而后利用AGCTNN与RBFNN两种反演模型对工程实例进行了反演分析。结果表明,提出的反演模型正耦合计算结果与实测值一致性更好,测点峰值加速度实测值与反演值的误差范围由8.73%~25.17%降至2.31%~8.16%,印证了该反演模型的合理性与应用于实际工程中的可能性。

Most existing parameter inversion methods of the dynamic constitutive model parameters do not take the concept of uncertainty into account.Therefore,the adaptive cloud transformation algorithm(AGCT)was combined with RBF neural network(RBFNN)to construct an adaptive cloud neural network parameter inversion model(AGCTNN),which converts the uncertainty concept into quantitative values and better takes into account the influence of the randomness and ambiguity between dam systems on the inversion of dynamic parameters.AGCT was compared and analyzed with three traditional clustering algorithms,K-Means,SOM and DBSCAN,to verify the superiority and feasibility of the algorithms.The inversion analysis was then carried out on engineering examples using two inversion models,AGCTNN and RBFNN.The results show that the positive coupling results of the proposed inversion model are in better agreement with the measured values,and the error range between the measured and inverse values of peak acceleration at measurement points is reduced from 8.73%-25.17%to 2.31%-8.16%,which confirms the reasonableness of the inversion model and the possibility of its application in practical engineering.