基于TimeGAN增強的CNN-LSTM模型在盾構(gòu)掘進地表沉降中的預測研究

摘要： 為更準確地預測小數(shù)據(jù)量下盾構(gòu)法施工造成的地表沉降,提出基于Time GAN(time series generative adversarial networks,時間序列生成對抗網(wǎng)絡(luò))增強的CNN(convolutional neural networks,卷積神經(jīng)網(wǎng)絡(luò))-LSTM(long short-term memory,長短期記憶網(wǎng)絡(luò))盾構(gòu)掘進地表沉降預測模型,并依托... （共10頁）