在隧道掘进机(TBM)施工中,准确的穿透率预测提高了破岩效率并减少了圆盘刀具的损坏。然而,该过程面临着巨大的挑战,例如地面条件的高度不确定性以及在大型和大型隧道中保持最佳TBM操作的复杂性。为了应对这些挑战,我们提出TCN-SENet++,一种新颖的混合多步实时渗透率预测模型,该模型结合了时间卷积网络(TCN)和挤压和激励(SENet)块,用于辅助隧穿。本研究旨在展示TCN-SENet++的应用,以及其他模型,如RNN,LSTM,GRU,和TCN,用于TBM渗透率预测。该模型是使用从Yin-Song引水项目中收集的实际数据集开发的。我们采用30秒的时间步长来预测渗透率的未来时间步长(1st,3rd,5th,Seven,和9th)。影响渗透率的特征,例如刀盘扭矩,推力,和刀盘功率,被考虑。使用平均绝对误差和均方误差进行比较分析,发现TCN-SENet++模型优于其他模型,包括RNN,LSTM,GRU,TCN,和TCN-SENet+。相比之下,TCN-SENet++实现了18%的平均MSE降低,6%,3%,1%,2%,分别。TCN-SENet++模型在新项目中显示出更少的错误,验证其在TBM施工中实时渗透率预测的有效性和适用性。
Accurate penetration rate prediction enhances rock-breaking efficiency and reduces disc cutter damage in tunnel boring machine (TBM) construction. However, this process faces significant challenges such as the high uncertainty of ground conditions and the complexity of maintaining optimal TBM operation in long and large tunnels. To address these challenges, we propose TCN-SENet++, a novel hybrid multistep real-time penetration rate prediction model that combines a temporal convolutional network (TCN) and a squeeze-and-excitation (SENet) block for aided tunneling. This study aims to demonstrate the application of TCN-SENet++, as well as other models such as RNN, LSTM, GRU, and TCN, for TBM penetration rate prediction. The model was developed using actual datasets collected from the Yin-Song diversion project. We employ a 30-s time step to predict the future time steps of the penetration rate (1st, 3rd, 5th, 7th, and 9th). The features that influence the penetration rate, such as the cutterhead torque, thrust, and cutterhead power, were considered. A comparative analysis using the mean absolute error and mean squared error revealed that the TCN-SENet++ model outperformed the other models, including RNN, LSTM, GRU, TCN, and TCN-SENet+. In comparison, TCN-SENet++ achieved average MSE reductions of 18%, 6%, 3%, 1%, and 2%, respectively. The TCN-SENet++ model demonstrated fewer errors in the new project, validating its effectiveness and suitability for real-time penetration rate prediction in TBM construction.