基于BP神经网络的堆石坝参数二次反演与变形预测

 在预测堆石坝长期变形时，常常需对堆石体流变参数进行反演。若同时对堆石体的瞬时变形力学参数和流变参数进行反演，则反演参数多，网络结构复杂，所需的训练样本数量大，反演效率低。根据堆石坝的监测资料，将堆石坝的沉降分解为瞬时沉降和流变引起的沉降，运用BP神经网络方法逐次增加训练样本，循环训练网络，将瞬时力学参数与流变参数分开来进行二次反演，训练样本少，反演效率高，输出结果用于预测能与监测资料较好吻合，可为类似工程提供参考和借鉴。

Two-step Back Analysis of Parameters and Deformation Prediction for Rock-Fill Dams Based on BP Neural Network

Back analysis of rheological parameters is usually necessary in the prediction of long-term deformation of rock-fill dams.In the case of the back analysis of transient deformation mechanical parameters and rheological parameters at the same time,the back analysis is inefficient owing to a large number of parameters and required training samples and complicated network structure.According to the monitoring data of a rock-fill dam,we classified the settlement into transient deformation and rheological settlement,and then employed BP neural network to increase training samples gradually and to train the network circularly,and back-analyzed the transient mechanical parameters and rheological parameters respectively in two steps.The settlement curves of forward analysis by using back-analyzed parameters were consistent with the monitored settlement curves.It’s predicted that settlement of the rock-fill dam tends to be stable three years after the normal storage level was reached,and the predicted maximum settlement is in accordance with the monitored data.The results indicate that the number of training samples is small,the inversion is efficient and the deformation prediction is reliable.