为建立高效的全断面硬岩隧道掘进机（TBM）掘进参数预测模型以辅助驾驶员进行参数调整，依托引绰济辽工程数据，先采用3σ准则、奇异值分解等方法对工程数据进行处理，再根据灰色关联度法对模型的输入参数进行筛选，接着将自注意力机制（SAM）融入到长短期记忆网络（LSTM）中，并使用Optuna程序对模型训练的最优超参数组合进行搜寻，最后构建出Optuna-SAM-LSTM预测模型。Optuna的10次搜寻试验结果表明： 1）模型使用该程序对搜寻得到的超参数组合进行训练均能取得较高的预测精度，且预测结果较为稳定。2)SAM-LSTM模型在对刀盘贯入度进行超前预测时，在未来3个时间步上均展现出高拟合度和低误差；该模型与LSTM、SAM-RNN和RNN模型的预测结果对比显示，SAM-LSTM模型的预测精度显著高于其他3个模型，并且融入自注意力机制的模型预测精度均高于普通模型。3)所提出的Optuna-SAM-LSTM模型在刀盘贯入度的超前预测中表现出色，在实际工程中可为驾驶员留出充足的决策时间来调整参数，以确保TBM施工的安全性和效率。
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.
