神经网络响应面在堆石坝流变反演中的应用

Application of neural network response surface in rheological inversion of rockfill dam

堆石体的流变参数对高面板堆石坝的长期安全性分析具有重要意义。参数反演可以准确获得符合坝体实际长期变形规律的流变参数。本文分别采用反向传播神经网络(BP)和径向基神经网络(RBF)构造出待反演参数与位移值之间的响应,引入统计学回归预测模型中的均方根误差(RMSE),平均绝对百分比误差(MAPE)和线性回归决定系数(R2)等指标来全面评估不同神经网络响应面映射能力的优劣,从而提高参数反演的效率和准确率。结果表明,RBF神经网络响应面的评估指标均优于BP神经网络响应面。利用RBF神经网络响应面和多种群遗传算法(MPGA)得到反演后的流变参数并将其用于有限元计算,得到的蓄集峡坝体沉降值在大小和分布上与实测值有很好的一致性。

Rheological parameters of rockfill are important for long-term safety analysis of high concrete face rockfill dams(CFRDs).Parameter inversion can accurately obtain rheological parameters to meet the practical long-term deformation law.This paper uses Backpropagation(BP)neural network and Radial basis function(RBF)neural network to construct the response between the parameters to be inverted and the measured displacement,and introduces the root mean square error(RMSE),the average absolute percentage error(MAPE),and the linear regression determination coefficient(R2)in the statistical regression prediction model to comprehensively compare the mapping capabilities of different neural network response surface.They can improve the efficiency and accuracy of parameter inversion.Results show that the evaluation indexes of RBF neural network response surface are better than those of BP neural network response surface.Therefore,we adopt RBF neural network response surface and multipopulation genetic algorithm(MPGA)to obtain the rheological parameters after inversion and use them for finite element calculation.It is found that the obtained settlement values of the Xujixia concrete face rockfill dam agree well with the measured ones both in magnitude and in distribution.