Real-Time Prediction of TBM Response Parameters Based on Temporal Convolutional Network

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Author(s)

  • Yuan-en Pang
  • Zi-kai Dong
  • Hong-wei Yu
  • Hao Cai
  • Guo-shuai Tian
  • And 4 others
  • Ji-Dong Yuan
  • Yan Liu
  • Yu Wang
  • Xu Li

Detail(s)

Original languageEnglish
Article number04024048
Journal / PublicationJournal of Computing in Civil Engineering
Volume39
Issue number1
Online published10 Oct 2024
Publication statusPublished - Jan 2025

Abstract

Establishing an accurate predictive model for response parameters is the foundation of control parameter optimization for tunnel boring machines (TBMs). However, existing research mostly focuses on mean values during stable stages, and lacks real-time prediction throughout the entire process, failing to meet the demand for fine-tuned parameter recommendations. This paper proposes the weight matrix method for feature selection, which provides specific numerical values and rankings of each feature's contribution. A deep learning model based on temporal convolutional network (TCN) is proposed to achieve real-time prediction of cutterhead torque (T) and total thrust (F), which is compared with the gated recurrent unit (GRU) and long short-term memory (LSTM). The proposed method was validated on the Yinchao project, and the results demonstrated that (1) the weight matrix method outperforms the Pearson coefficient method in terms of model accuracy, and (2) the TCN model performs better than GRU and LSTM. The method proposed in this paper achieves high precision in predicting T and F, and holds promise as a core algorithm for automatic control in TBM and providing crucial support for TBM's advancement into the era of autonomous driving. © 2024 American Society of Civil Engineers.

Research Area(s)

  • Feature selection, Real-time prediction, TBM, TCN

Citation Format(s)

Real-Time Prediction of TBM Response Parameters Based on Temporal Convolutional Network. / Pang, Yuan-en; Dong, Zi-kai; Yu, Hong-wei et al.
In: Journal of Computing in Civil Engineering, Vol. 39, No. 1, 04024048, 01.2025.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review