Long Li

Predicting cutterhead torque is essential for optimizing TBM construction strategies and minimizing jamming risks. This study presents a novel hybrid model (IEWOA-TSVD-ITELM), developed using data from 7635 tunneling cycles from the Yinsong Project, to enhance cutterhead torque prediction accuracy. The EWOA enhances its global search capability by introducing new position updating and adaptive adjustment strategies (IEWOA). In addition, by leveraging the Softsign function for the nonlinear transformation of the expected output matrix of the TELM, a third hidden layer is added to enhance the feature extraction capabilities (ITELM), whereas truncated singular value decomposition (TSVD) is employed to reduce the noise in the output matrix of the third hidden layer of the ITELM (TSVD-ITELM). Furthermore, the IEWOA optimized the number of neurons and randomly generated weights and biases in the TSVD-ITELM. This study comprehensively evaluates and compares six optimization algorithms using 25 standard test functions. Additionally, the IEWOA-TSVD-ITELM is compared with eight classical machine learning models. This study examines the impact of different timing lengths of the rising phase and rock mass grades on model performance. The results demonstrate the outstanding performance of the IEWOA as an optimization algorithm. The IEWOA-TSVD-ITELM model achieves an R ² value of 0.644 on the test set, with an MAE of 326.623 and an RMSE of 435.821, outperforming the other algorithms. Increasing the timing length from 30 to 60 s reduces the MAE and RMSE by 11.82% and 9.56%, respectively, but the gains diminish when the timing length increases from 60 to 90 s.

Accurate penetration rate prediction enhances rock-breaking efficiency and reduces disc cutter damage in tunnel boring machine (TBM) construction. However, this process faces significant challenges such as the high uncertainty of ground conditions and the complexity of maintaining optimal TBM operation in long and large tunnels. To address these challenges, we propose TCN-SENet++, a novel hybrid multistep real-time penetration rate prediction model that combines a temporal convolutional network (TCN) and a squeeze-and-excitation (SENet) block for aided tunneling. This study aims to demonstrate the application of TCN-SENet++, as well as other models such as RNN, LSTM, GRU, and TCN, for TBM penetration rate prediction. The model was developed using actual datasets collected from the Yin-Song diversion project. We employ a 30-s time step to predict the future time steps of the penetration rate (1st, 3rd, 5th, 7th, and 9th). The features that influence the penetration rate, such as the cutterhead torque, thrust, and cutterhead power, were considered. A comparative analysis using the mean absolute error and mean squared error revealed that the TCN-SENet++ model outperformed the other models, including RNN, LSTM, GRU, TCN, and TCN-SENet+. In comparison, TCN-SENet++ achieved average MSE reductions of 18%, 6%, 3%, 1%, and 2%, respectively. The TCN-SENet++ model demonstrated fewer errors in the new project, validating its effectiveness and suitability for real-time penetration rate prediction in TBM construction.

It is difficult for tunnel boring machine (TBM) operators to respond for safe and high-efficient construction without accurate reference parameters such as the TBM thrust. A new hybrid model (MRFO-AT-TELM) combining an improved two-hidden-layer extreme learning machine (AT-TELM) and manta ray foraging optimization (MRFO) algorithm is proposed to predict TBM thrust with 12 selected input featuring parameters. The affine transformation (AT) activation function is used to improve the performance of TELM. Input weights and bias of AT-TELM are optimized using the MRFO algorithm. The performance of the proposed model is validated with TBM construction data collected from the Yin-Song Project in China and compared with other models. Input data of the first 30, 60, and 90 seconds of the rising period are analyzed. Results show that the proposed model is superior to the other models and with 90-second data as input outperforms that with 30 and 60-seconds data. The proposed model and the selected input features are validated in a new project. The thrust prediction model can be embedded into the TBM construction intelligence system and thus help improve construction efficiency.

Foam-cemented paste backfill (FCPB) has become a trend to solve the problem of roof-contacted filling. In order to solve the time-consuming and labor-intensive disadvantages when a large number of uniaxial compressive strength (UCS) tests needed, a hybrid artificial intelligence model was proposed for FCPB strength prediction, which combines random forest (RF) algorithm and grid search optimizer (GSO). Moreover, the effects of foaming agents on cement hydration and pore characteristics were studied. The results showed that GSO can effectively tune the hyper-parameters of the proposed GSO-RF model and the developed model is an efficient and accurate method to predict the UCS for FCPB. Though the foaming agent will not change the influence trend of cement-tailings ratio, solid content and curing time, the influence degree will be weakened by the foaming agent. The ranking of the relative importance of influencing variables is: cement-tailings ratio > curing time > foaming agent dosage > solid content. In addition, the foaming agent has no significant effect on the hydration of cement. The foaming agent mainly changes the strength by changing the pore characteristics (especially the large pore volume). This research provided guidance for developing UCS prediction model for FCPB.