Explore the vast range of titles available.

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.

Argillization is commonly observed as excavating in mudstone stratum by tunnel boring machine. In addition to the operational and geological parameters studied by previous researchers, this phenomenon also has significant influence on the performance of tunnel boring machine, such as penetrate rate, advance rate, and utilization rate. In general, water is a key factor affecting the progress of argillization. With the aim to investigate the effect of water content on argillization of mudstone during the tunnelling, a new rolling abrasion test was conducted on rock blocks with moisture contents of 2.82%, 3.44%, 4.79%, 6.06%, and 6.75%. In the experiment, penetration depth, temperature fields of disc cutter and rock blocks, and wear loss of cutters were recorded. In addition, the microstructures of cutting groove on rock blocks and slacking mudstone were observed by OLYMPUS SZX16 stereomicroscope. According to experimental results, three stages of argillization process can be divided: (1) water evaporation of mudstone nearby the disc cutter, (2) destruction of microstructure of mudstone, and (3) formation of slaking mudstone. Uneven shrink, water-weakening effects, temperature effects, and mechanical activation are mainly contributed to the damage of microstructures of rock blocks. In addition, the variation in water content accelerates the argillization process. By comparison, it is found that wear loss of disc cutter and cutting efficiency show negative and positive correlation with the extent of argillization, respectively. However, flat wear appears due to the argillization. Therefore, in engineering practice, to obtain high work efficiency of tunnel boring machine, it is necessary to keep water content of clay-bearing rock in a reasonable range. This study reveals the argillization process and abnormal cutter wear mechanism from the microstructure’s perspective. In addition, the effects of temperature, water, and mechanical motion are simultaneously taken into consideration. The present study provides some references for reasonably improving tunnel boring machine performance in the tunnel construction.

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.

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.

Numerous rockbursts controlled by small-scale structural planes have occurred frequently during tunnel boring machine (TBM) excavation in a headrace tunnel. To understand the evolutionary process of structure-type rockbursts, a real-time microseismic (MS) monitoring system was deployed during the advancement of TBM. By combination with the true reflection tomography technique, a new method is proposed to estimate the P-wave velocity for in situ hypocentral locations. A typical structure-type rockburst is investigated to study the relationship between the rockburst characteristics and microseismicity. By further analyzing the temporal–spatial distribution of microseismicity and the quantitative interpretation of the MS source parameters, the potential failure zone and the precursor features are recognized during the development of this structure-type rockburst. Based on the MS monitoring results, some proactive treatment measures are put forward for the mitigation of rockburst hazards. The results of the current research can contribute to the understanding of structure-type rockbursts and provide valuable references for rockburst forewarning and construction management in similar tunneling projects.

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.

Disc cutters mounted on the cutterhead of tunnel boring machine (TBM) can be divided into center cutter, face cutter and gauge cutter according to their installment radius. Due to the differences in rock cutting condition, disc cutter turning radius and cutterhead stiffness distribution, they show very different characteristics in the magnitude of disc cutter normal and rolling forces. For the realistic modeling of whole cutterhead, full-scale rotary cutting test using disc cutter with different installment radius is a useful and inspiring way to study the influence of disc cutter installment radius on rock cutting forces. By conducting full-scale rotary cutting test, two main conclusions are obtained. First, disc cutter normal and rolling forces both decrease rapidly and then remain nearly stable when disc cutter installment radius increases. Second, the normal and rolling forces of the innermost disc cutters are two to four times higher than those of the outermost disc cutters. This study verifies the very uneven distribution of the disc cutter cutting forces on the cutterhead, and it can contribute to better arrangement of the disc cutters to ensure that they subject to similar loading and wear conditions and also can offer suggestions for field TBM operation to avoid severe overloading of the inner disc cutters.

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.

Tunnel Boring Machine (TBM) penetration rate prediction is one of the most important problem in tunneling projects. Estimating of Tunnel Boring Machine (TBM) penetration rate can considerably reduce the costs of tunneling projects. In this study, Datasets including Uniaxial Compressive Strength, Brazilian Tensile Strength, Density and Joint Angle as input parameters and Rate of Penetration as an output parameter. The aim of this study is estimating the penetration rate of tunnel boring machines using fuzzy logic method, Harmony search algorithm (HSA) and Particle Swarm Optimization (PSO) in the Nosoud water conveyance Tunnel. The modeling results showed that the fuzzy model has a significant advantage over the PSO and HSA.

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.