Sign in

Paper Video Conference

Subscribe Submit

  • Home
  • Journals
  • Focus
  • Videos
  • Achievement
  • Fronts
  • Contact Us
Journal Home Online First Current Issue Archive For Authors Journal Information 中文版

2022, Volume 16, Issue 1

Outline

Abstract

Keywords

Frontiers of Structural and Civil Engineering doi: 10.1007/s11709-022-0823-3

Realtime prediction of hard rock TBM advance rate using temporal convolutional network (TCN) with tunnel construction big data

Show More

Available online:2022-06-27

Abstract

Real-time dynamic adjustment of the tunnel bore machine (TBM) advance rate according to the rock-machine interaction parameters is of great significance to the adaptability of TBM and its efficiency in construction. This paper proposes a real-time predictive model of TBM advance rate using the temporal convolutional network (TCN), based on TBM construction big data. The prediction model was built using an experimental database, containing 235 data sets, established from the construction data from the Jilin Water-Diversion Tunnel Project in China. The TBM operating parameters, including total thrust, cutterhead rotation, cutterhead torque and penetration rate, are selected as the input parameters of the model. The TCN model is found outperforming the recurrent neural network (RNN) and long short-term memory (LSTM) model in predicting the TBM advance rate with much smaller values of mean absolute percentage error than the latter two. The penetration rate and cutterhead torque of the current moment have significant influence on the TBM advance rate of the next moment. On the contrary, the influence of the cutterhead rotation and total thrust is moderate. The work provides a new concept of real-time prediction of the TBM performance for highly efficient tunnel construction.

Keywords

hard rock tunnel ; tunnel bore machine advance rate prediction ; temporal convolutional networks ; soft computing ; construction big data

Content

关注我们

Website Copyright © 2015 China Engineering Science Press Co., Ltd.

京公网安备 11010502051620号 京ICP备11030251号-2
Follow us
Website Copyright © 2015 China Engineering Science Press Co., Ltd.
京公网安备 11010502051620号 京ICP备11030251号-2