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Full-face tunnel boring machine (TBM) is a modern and efficient tunnel construction equipment. A reliable and accurate TBM performance (like penetration rate, PR) prediction can reduce the cost and help to select the appropriate construction method. Therefore, this study introduces a new hybrid intelligence technique, i.e., grey wolf optimizer-feature weighted-multiple kernel-support vector regression (GWO-FW-MKL-SVR) to predict TBM PR. For this purpose, a tunnel in China was selected as a case study and the most important parameters on TBM performance, i.e., chamber earth pressure, total thrust, cutterhead torque, cutterhead speed, cohesion, internal friction angle, compression modulus, the ratio of boulder, uniaxial compressive strength and rock quality designation, were measured and considered as model inputs. To show the capability of the GWO-FW-MKL-SVR model, three models including biogeography-based optimization (BBO)-FW-MKL-SVR, MKL-SVR, and SVR were also proposed to predict the TBM PR. To select the best predictive models, some performance indices, i.e., coefficient of determination (R2), root mean square error (RMSE) and variance accounted for (VAF) were considered and calculated. The obtained results showed that the GWO-FW-MKL-SVR model receives the highest accuracy in predicting the TBM PR for both train and test stages. R2 values of 0.946 and 0.894, for train and test stages of the GWO-FW-MKL-SVR model, respectively, confirmed that this new hybrid model is considered as a powerful, applicable and simple technique in predicting the TBM PR. By performing feature weight analysis, it was found that the effects of the uniaxial compressive strength, rock quality designation and cutterhead speed features were higher than the other input parameters on the TBM PR.

As a critical component of a tunnel boring machine (TBM), the precise condition monitoring and fault analysis of the main bearing is essential to guarantee the safety and efficiency of the TBM cutter drive. Currently, under conditions of strong noise and complex working environments, traditional signal decomposition and machine learning methods struggle to extract weak fault features and achieve high fault classification accuracy. To address these issues, we propose a novel residual denoising and multiscale attention-based weighted domain adaptation network (RDMA-WDAN) for TBM main bearing fault diagnosis. Our approach skillfully designs a deep feature extractor incorporating residual denoising and multiscale attention modules, achieving better domain adaptation despite significant domain interference. The residual denoising component utilizes a convolutional block to extract noise features, removing them via residual connections. Meanwhile, the multiscale attention module uses a 4-branch convolution and 3 pooling strategy-based channel-spatial attention mechanism to extract multiscale features, concentrating on deep fault features. During training, a weighting mechanism is introduced to prioritize domain samples with clear fault features. This optimizes the deep feature extractor to obtain common features, enhancing domain adaptation. A low-speed and heavy-loaded bearing testbed was built, and fault data sets were established to validate the proposed method. Comparative experiments show that in noise domain adaptation tasks, proposed the RDMA–WDAN significantly improves target domain classification accuracy by 42.544%, 23.088%, 43.133%, 16.344%, 5.022%, and 9.233% over dense connection network (DenseNet), squeeze-excitation residual network (SE-ResNet), antinoise multiscale convolutional neural network (ANMSCNN), multiscale attention module-based convolutional neural network (MSAMCNN), domain adaptation network, and hybrid weighted domain adaptation (HWDA). In combined noise and working condition domain adaptation tasks, the RDMA–WDAN improves the accuracy by 45.672%, 23.188%, 43.266%, 16.077%, 5.716%, and 9.678% compared with baseline models.

The RADIX (Rapid Access Drilling and Ice eXtraction) optical dust logger is part of the exploratory drilling system developed at the University of Bern. It was previously untested because no RADIX borehole reached the depth of the required bubble-free ice. In June 2023, we fitted the logger with an adapter to enable operation and testing in the deep EastGRIP (East Greenland Ice-core Project) borehole. A high-quality dust record was obtained for the Bølling-Allerød-Younger Dryas-Early Holocene period. The light scattered by the dust in the ice around the borehole was slightly higher than the detection range of the logger, requiring a reduction in the sensitivity for future deployments.

To investigate the efficacy of laser as an auxiliary rock breaking technology and its compatibility with TBM (tunnel boring machine) tools, we conducted three experimental series with different laser power levels and scorching durations. Static tests showed that higher laser power led to greater rock-breaking depth. The relationship between laser power and kerf depth was linear for short irradiation times but followed a logarithmic trend for longer times. However, laser assisted by high-pressure gas blowing rock breaking showed a consistent linear increase in depth with rising laser power. Mobile laser rock breaking tests demonstrated decreased kerf depth with increased movement speed. Numerical simulations using a thermal–mechanical coupling model confirmed that longer laser scorching times led to more extensive internal cracks in rocks, primarily shear cracks, and reduced the force needed for hob rock breaking. This research suggested the feasibility of laser technology as a complement to TBM hobs for rock breaking. Highlights This study investigated the effects of laser power, irradiation time, high-pressure gas blowing and laser movement speed on laser irradiation breaking granite Blowing high-pressure gas-assisted laser irradiation rock can improve kerf depth markedly. Laser irradiation rock induced mainly shear cracks and a minority of tensile cracks. Hob rock breaking simulation suggested that 5–10 s laser irradiation is optimal.

Studying the disturbance patterns of surrounding rock and soil during TBM excavation holds great significance in ensuring the safe construction of tunnels. Geomechanical model testing serves as an effective approach for such research. However, the current model tests suffer from lengthy curing times of rock similar materials and the simplification of tunneling simulation devices. To address these issues, this paper presents an innovative approach by preparing a rock similar material with early strength and good brittleness and utilizing a self-developed scaled TBM testing machine for rock breaking simulations. The conducted tests demonstrate that an increased content of sulphoaluminate cement significantly enhances the compressive-tensile ratio of the rock-like similar materials, thus indicating the excellent performance of the developed materials. The impact zone of TBM tunneling primarily encompasses the surrounding rock within 0.5D in front of the tunnel face and 2D at the rear. The proposed method for similar material preparation and the model test device put forward in this paper can serve as valuable references for similar tunneling experiments.

In this paper the propagation of smoke was studied experimentally in a tunnel in the case of a fire occurring during excavation, for various slopes from 0° to 5°. The tunnel is closed on the side of the Tunnel Boring Machine, with no specific ventilation system to control the smoke. The analysis of the results focused on the evolution of the smoke layer height in the gallery, for different position in the tunnel under various slopes. It has been found that smoke can fill the section of the tunnel by 65 % in certain cases.

Tunnel boring machines (TBMs), excavation tools for tunnel construction, often struggle to penetrate extremely hard rock formations. This study proposes an innovative solution to enhance the construction efficiency of TBMs in such environments: a microwave-assisted TBM cutter. In our experiments, we irradiated basalt samples with 3 kW microwaves for varying durations. We conducted a full-scale rock-breaking experiment using the multifunctional rock-breaking platform at Central South University, China. We then compared the cutting force, cutting coefficient, and specific energy consumption of the cutter at different processing times. Our results indicate that microwave pretreatment can effectively reduce both the cutting force and specific energy consumption of rock. Furthermore, under certain cutting force conditions, the quantity of rock broken can be significantly increased with extended microwave irradiation. However, we found a critical threshold for the processing time of 3 kW microwave-assisted rock breaking. Beyond this threshold, the specific energy consumption of rock breaking is not significantly reduced. For a microwave processing time of 16 min, the optimal ratio of cutter spacing to penetration ( s/p) is 16.7. The findings of this study offer valuable insights and guidance for TBM construction operations in extremely hard-rock environments.

The tunnel boring machine (TBM), developed within the past few decades, is designed to make the process of tunnel excavation safer and more economical. The use of TBMs in civil and mining construction projects is controlled by several factors including economic considerations and schedule deadlines. Hence, improved methods for estimating TBM performance are important for future projects. This paper presents a new model based on the group method of data handling (GMDH) for predicting the penetration rate (PR) of a TBM. In order to achieve this aim, after investigation of the most effective parameters of PR, rock quality designation, uniaxial compressive strength, rock mass rating, Brazilian tensile strength, weathering zone, thrust force per cutter and revolutions per minute were selected and measured to estimate TBM PR. A database composed of 209 datasets was prepared according to the mentioned model inputs and output. Then, based on the most influential factors of GMDH, a series of parametric investigations were carried out on the established database. In the following, five different datasets with different sets of training and testing were selected and used to construct GMDH models. Aside from that, five multiple regression (MR) models/equations were also proposed to predict TBM PR for comparison purposes. After that, a ranking system was used in order to evaluate the obtained results. As a result, performance prediction results of [i.e. coefficient of determination (R2) = 0.946 and 0.924, root mean square error (RMSE) = 0.141 and 0.169 for training and testing datasets, respectively] demonstrated a high accuracy level of GMDH model in estimating TBM PR. Although both methods are applicable for estimation of PR, GMDH is able to provide a higher degree of accuracy and can be introduced as a new model in this field.

In the process of hard rock tunnel excavation, workers often need to enter the tunnel boring machine (TBM) cutterhead at regular intervals to measure cutter wear. However, this method is time-consuming and labor-intensive. Existing cutter prediction models primarily rely on geological parameters to predict overall cutter wear before construction, making it challenging to monitor real-time wear at different locations of the cutter and obtain accurate geological parameters. To address these challenges, this paper proposes a multi-head mixed attention mechanism-based method for real-time wear prediction of TBM disc cutter. First, a method of cutter wear normalization to eliminate measurement noise is explored. Then, considering the complex correlation of TBM operating parameters in feature and time dimensions, a new multi-head mixed attention mechanism model is designed to establish the dependency between different features and different moments, to better establish the mapping model between operating parameters and cutter wear. Finally, the current cutter wear state can be calculated by accumulating the wear amount of all previous small excavation sections. The effectiveness of the method is verified by using field data from the Mumbai tunnel. The results demonstrate that the method is capable of real-time prediction of front cutter and edge cutter wear on the test set, achieving an average accuracy rate of 95.75%. Moreover, the method can update the cutter wear status after every meter of excavation, which has good real-time performance. In addition, the average accuracy of cutter wear prediction of the proposed method is 11.75%, 10.375%, 3.875%, 3.625%, 1.375%, 3.125%, 2.25%, and 0.75% higher than that of LSTM, CNN, LSTM-CNN, CACNN, SACNN, MMADNN, MTACNN, and MFACNN. In summary, this approach offers an accurate prediction of cutter wear state while reducing inspection time and costs and has high application value.

In this paper, water jet-guided laser (WJGL) drilling of C[sub.f]/SiC composites was employed and the effects of the processing parameters on the depth and quality of the micro-holes were systematically investigated. Firstly, the depth measurement showed that the increase in processing time and power density led to a significant improvement in micro-hole drilling depth. However, the enhancement of the water jet speed resulted in a pronounced decrease in the depth due to the phenomenon of water splashing. In contrast, the scanning speed, path overlap ratio, pulse frequency, and helium pressure exhibited less effect on the micro-hole depth. Secondly, the microstructural analysis revealed that the increase in power density resulted in the deformation and fracture of the carbon fibers, while the augmentation in water jet speed reduced the thermal defects. Finally, based on the optimization of the processing parameters, a micro-hole of exceptional quality was achieved, with a depth-to-diameter ratio of 8.03 and a sidewall taper of 0.72°. This study can provide valuable guidance for WJGL micro-hole drilling of C[sub.f]/SiC composites.