Numerical simulation of rock fragmentation process induced by two TBM cutters and cutter spacing optimization

Water can greatly influence the mechanical properties and dynamic fracture of underground rock mass. However, the dynamic fragmentation and chip formation of water-soaked rock in linear cutting is neglected. In this work, a coupled moisture migration-fracture model is utilized to investigate this problem. First, the water weakening effect on rock mechanical parameters is introduced in the coupled model according to a fitting laboratory test. Then, the uniaxial tensile, uniaxial compression, triaxial compression, and swelling experiments are utilized to calibrate the input micro-parameters. Subsequently, a series of scratch tests are carried out on the water-soaked rock samples. The stress evolution, fragmentation mode, cutting force, crack number, extent of fracture zone, chip shape, chip area, cutting work, and mechanical specific energy (MSE) with different water content are studied. The results indicate that as water content increases, the rock sample changes from brittle failure to ductile failure in linear cutting, the peak cutting force gradually decreases, and the chip area first becomes larger and then smaller, while the MSE shows an opposite trend. Especially, a water content of 2% can bring the largest chip area and the highest cutting efficiency. Moreover, the effects of cutting velocity and cutting depth on the above indexes are discussed. Finally, a complex specimen composed of half wet and half dry rock is built to further demonstrate the influence of water on rock dynamic fragmentation and chip formation in linear cutting. The modeling results in this paper provide new insights into the influence mechanism of water on rock dynamic fragmentation, chip formation, and cutting efficiency in linear cutting.

To compare the indentation behaviors and the rock fracture mechanism induced by different types of indenters, the indentation experiments and simulations of the CCS, SCS, and TCS indenters are performed. Then, the indentation force, rock fracture state, rock chip, and the specific energy, etc. caused by three indenters are analyzed. The research results indicate that the SCS and TCS indenters with teeth can penetrate rock with a concentrated stress, and reduce the indentation force and specific energy significantly compared with the CCS indenter. The indentation behavior of the three indenters is significantly influenced by the indentation spacing. With the rise of the indentation spacing, the indentation difficulty presents a trend of first increasing and then stabilizing for three indenters, but the specific energy exhibits a pattern that first goes down, then up, and finally stabilizing. There is an optimal indentation spacing for the CCS, SCS, and TCS indenters to reaches the lowest specific energy, which is approximately 30, 30 and 20 mm, respectively. Additionally, compared with tapered teeth on the TCS indenter, spherical teeth on the SCS indenter can crush rock with a more synergistic action. The researches can be used as a guide for the design of excavation tools for crushing rock.

Cutter spacing is one of the key factors affecting the rock breaking efficiency of Tunnel Boring Machine (TBM). In the excavation of deep soft rock tunnels, the water content can change the fragmentation behavior by TBM cutters, and further influence rock breaking efficiency. This study takes mudstone as an example to study the influence mechanism of water content on the optimum cutter spacing and rock breaking efficiency when TBM disc cutters penetrate soft rock. Firstly, the FDEM water migration-fracture coupled model considers the water weakening effect based on existing experimental fitting results. Secondly, several numerical experiments are conducted to calibrate the microscopic parameters input in FDEM. Finally, a series of numerical experiments of TBM double disc cutters penetrating in water-soaked mudstones are carried out using the coupling model. The crushing characteristics, cutter force, acoustic emission (AE), and crack pattern with different water content and cutter spacing are discussed in detail. Moreover, a novel recognition algorithm is proposed to calculate the rock chip distribution and area. The crack number, work done by the cutters, and specific energy (SE) are quantitatively analyzed. The results indicate that the water content of 2%–3% and the cutter spacing of 60–80 mm can lead to the lowest SE and highest rock breaking efficiency. Overall, the optimum cutter spacing decreases with the increase of water content. The modeling results can supply guidance for the optimization of cutter spacing during TBM tunneling in water-rich soft rock ground.

This review summarizes the research outcomes and findings documented in 45 journal papers using a shared tunnel boring machine (TBM) dataset for performance prediction and boring efficiency optimization using machine learning methods. The big dataset was collected during the Yinsong water diversion project construction in China, covering the tunnel excavation of a 20 km-section with 199 items of monitoring metrics taken with an interval of one second. The research papers were the result of a call for contributions during a TBM machine learning contest in 2019 and covered a variety of topics related to the intelligent construction of TBM. This review comprises two parts. Part I is concerned with the data processing, feature extraction, and machine learning methods applied by the contributors. The review finds that the data-driven and knowledge-driven approaches in extracting important features applied by various authors are diversified, requiring further studies to achieve commonly accepted criteria. The techniques for cleaning and amending the raw data adopted by the contributors were summarized, indicating some highlights such as the importance of sufficiently high frequency of data acquisition (higher than 1 second), classification and standardization for the data preprocessing process, and the appropriate selections of features in a boring cycle. The review finds that both supervised and unsupervised machine learning methods have been utilized by various researchers. The ensemble and deep learning methods have found wide applications. Part I highlights the important features of the individual methods applied by the contributors, including the structures of the algorithm, selection of hyperparameters, and model validation approaches.

In this paper, the influence of rock type and uniaxial compressive strength on the cutter penetration is investigated to provide a basic guideline for the optimum field cutter spacing. The successfully utilized cutterhead layout designs are also studied in various rock types and different categories of tunnel sizes for projects with relatively high performance. The results of the conducted analyses show that the maximum cutter penetration in uniaxial compressive strength values below 50 and above 150 MPa is achieved when the cutter spacing is close to 90 and below 80 mm respectively. The study on the layout design characteristics of the cutterhead shows the evenly distributed scheme is more used with success even in softer rocks (when the rock mass condition is good). In softer rocks, the extension of the openings may have to be well over 50% of the cutterhead radius to maximize its performance. It is also found that the linear speed of the cutters has a direct correlation with two major parameters of normal force index (NFI) and rolling force index (RFI). In this regard, two formulas are generated using statistical analysis of the data from around 260 tunnel projects to evaluate both NFI and RFI. The corresponding formulas have coefficients of determination of 77 and 68% respectively. These formulas are used in an optimization process to maximize cutter penetration using various operational constraints (cutter load capacity, cutterhead torque limit, cutter geometry constrains, and cutterhead penetration rate limits). The new findings of this study can provide a foundation to improve the design process of hard rock TBMs and to optimize their performance considering various project setting parameters.

There are many studies on rock-breaking efficiency of TBM's disc cutters on homogeneous rocks; however, there are few studies on the cutter's rock-breaking efficiency on composite rocks. Specifically, research on the breaking efficiency of cutters on composite rocks with substantial differences in strength and composite ratios has not yet been systematically reported. The aim of this study was to numerically simulate the rock-breaking process of the TBM disc cutters to investigate the performance of the cutters on composite rocks. The underlying numerical simulation was validated by our full-scale laboratory tests. A series of numerical simulations were shown to lead to an accurate determination of the disc cutters performance on composite rocks of sandstone and granite in terms of the normal force, torque and specific energy. This indicates that there are some similarities between homogeneous and composite rocks, e.g., both show an optimal ratio of cutter spacing, s, to penetration depth, p, i.e., optimal s/p. Nevertheless, there are many more significant differences demonstrated in our study, including the composite ratio-dependent different ranges of the cutter force, torque and specific energy, changes of the optimum s/p, and the characteristic that s/p decreases linearly with the increase of the composite ratio. Although the optimal s/p is different for different composite ratios, it is possible to obtain a representative optimal s/p, e.g., 14, in our case, i.e. composite rocks of sandstone and granite, which may approximately optimise the composite rock-breaking, irrespective of different composite ratios. The paper also suggests that it is possible to study the rock-breaking efficiency of the disc cutters on composite rocks with appropriate consideration of composite UCS.