基于DNN的盾构施工地层横向水平变形预测

Prediction of lateral horizontal deformation of surrounding strata caused by shield construction based on deep neural network

为了得到盾构施工地层横向水平变形的有效预测模型,提出一种考虑主要盾构施工参数的深度神经网络(deep neural networks,DNN)模型。由广州某盾构工程得到盾构施工引起的周边地层水平变形数据,利用现场实测数据和主要盾构施工参数对模型进行训练并结合遗传算法优化DNN模型网络拓扑结构。结合遗传算法计算80个模型后,最终确定DNN模型在训练过程中均方误差MSE经50轮迭代即收敛并趋于零值,模型预测值和实测值基本吻合(拟合优度R2>0.9),且残差随机分布于零值线附近,说明该模型预测效果良好。研究结果可为类似工程构建水平变形预测模型提供参考。

In order to obtain an effective prediction model for the horizontal deformation of the strata under shieldconstruction,a deep neural network(DNN) model considering the main construction parameters was proposed. The lateral horizontal deformation data of surrounding strata caused by shield construction was obtained from a certain project in Nansha District,Guangzhou. A deep neural network model optimized with genetic algorithm,trained with the field measured data and the main construction parameters,was established. After training 80 models with genetic algorithm,the mean square error(MSE) of DNN model converged to zero in 50 rounds of iteration in the training process and the residuals randomly distributed near the zero line. Moreover,the predicted value was basically consistent with the measured value and determination coefficients R2 were above 0. 9. The study results could provide a reference for the construction of lateral horizontal deformation prediction model of similar projects.