Prediction of Shield Tunneling-Induced Surface Settlement Using Time Series Generative Adversrial Networks Enhanced Convolutional Neural Networks-Long Short-Term Memory Model
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万方数据
维普
为更准确地预测小数据量下盾构法施工造成的地表沉降,提出基于TimeGAN(time series generative adversarial networks,时间序列生成对抗网络)增强的CNN(convolutional neural networks,卷积神经网络)-LSTM(long short-term memory,长短期记忆网络)盾构掘进地表沉降预测模型,并依托上海北横通道新建工程Ⅱ标盾构施工项目验证该增强模型的性能.首先,选取300环的部分施工参数、地质参数、几何参数以及地表最大沉降,对比LSTM、CNN-LSTM与TimeGAN-CNN-LSTM的性能,证明CNN-LSTM对于盾构施工环境下多参数的预测效果明显优于LSTM,TimeGAN-CNN-LSTM增强模型优于CNN-LSTM;然后,通过更改训练集及测试集的大小,对不同数据集下TimeGAN-CNN-LSTM增强模型相较CNN-LSTM的预测效果进行研究.结果表明:TimeGAN-CNN-LSTM增强模型预测效果相较CNN-LSTM模型提升显著,且当训练集与测试集比值为4～8时,提升最为显著.
The authors present a predictive model for shield tunneling-induced surface settlement using a convolutional neural networks(CNN)-long short-term memory(LSTM)framework enhanced by time series generative adversarial networks(TimeG AN).This model is designed to deliver accurate predictions even with limited data volumes.The model's effectiveness is validated through its application to the section Ⅱ of the Beiheng tunnel project in Shanghai,China.Key parameters,including construction,geological,and geometric data along with maximum surface settlement are selected to evaluate the prediction performance.Comparative analysis shows that CNN-LSTM outperforms LSTM alone,while the TimeGAN-CNN-LSTM model provides superior accuracy over CNN-LSTM.In addition,experiments adjusting the training and test data ratios reveal that the TimeGAN-CNN-LSTM model significantly improves prediction accuracy,particularly when the training-to-test set ratio is 4-8.
万方数据
维普
