To develop an efficient
prediction model for tunneling parameters in full-face hard rock tunnel boring machines (TBMs) that can assist real-time parameter adjustments, a case study is conducted on the Chao′er river to Liaoxi river water-diversion
project, and the data from the project are rigorously processed using the 3σ criterion and singular value decomposition method. Following this,
the input parameters are selected based on the grey relational analysis method.
To enhance the predictive capabilities of the model, a self-attention mechanism (SAM) is integrated into a long short-term memory (LSTM) network, and an Optuna framework is then employed
to search the optimal combination of hyperparameters, thus finally establishing
an OptunaSAM-LSTM prediction
model. The effectiveness of the Optuna-SAM-LSTM model is validated through 10 search trials conducted using the
Optuna framework. The results consistently demonstrate the following: (1) The
model trained with the hyperparameters identified by Optuna achieves high
predictive accuracy and exhibits remarkable stability across different trials.
(2) When predicting the penetration rate of the TBM cutterhead, the SAM-LSTM model displays a high degree of fit and low prediction error
over the next three time steps, indicating its robustness in forecasting key
operational parameters. Comparative analysis with other models, including LSTM,
SAM-recurrent neural network (RNN), and RNN, reveals
that the SAM-LSTM model significantly outperforms these
alternatives in terms of prediction accuracy. Furthermore, models incorporating
the SAM consistently achieves higher accuracy than their conventional
counterparts, underscoring the value of this approach. (3) The Optuna-SAM-LSTM
model demonstrates exceptional performance in the advance prediction of TBM
cutterhead penetration rate. Its application in real-world
engineering projects can provide operators with ample decision-making time to adjust parameters, thereby ensuring the safety and
efficiency of TBM operations.
LIU He
,
MAN Ke
,
LIU Xiaoli
,
SONG Zhifei
. Cutterhead Penetration Rate
Prediction of Tunnel Boring Machines Based on Optuna-Self-Attention
Mechanism-Long Short-Term
Memory Model[J]. Tunnel Construction, 2024
, 44(S2)
: 329
-341
.
DOI: 10.3973/j.issn.2096-4498.2024.S2.032
