Multi-step Real-time Prediction Of Hard-rock Tbm Penetration Rate Combining Temporal Convolutional Network And Squeeze-and-excitation Block

This article discusses the challenges faced in predicting the penetration rate in tunnel boring machine (TBM) construction and proposes a novel hybrid model, TCN-SENet++, for real-time prediction. The model combines a temporal convolutional network (TCN) and a squeeze-and-excitation (SENet) block and outperforms other models in terms of accuracy. The effectiveness of the model is validated through a comparative analysis and its application in a real project.