A new method for selecting hard rock TBM tunnelling parameters using optimum energy: A case study

Xue, Yadong; Feng, Zuren; Zhao, Hanxiang; Li, Xing; Diao, Zhen-xing

doi:10.1016/j.tust.2018.03.030

Cited by 44 publications

(5 citation statements)

References 18 publications

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“…Although the rock breakage volume, depth, and diameter are important for estimating the rock fragmentation performance, the specific energy consumption is a comprehensive index for evaluating the rock fragmentation ability of mechanical tools, such as a conical pick, drag pick, and disc cutter [39,40]. According to (1), the specific energy consumption of the rock breakage by different indenter shapes and impact energies is shown in Figure 13.…”

Section: Rock Breakage Performance Under Different Indentermentioning

confidence: 99%

Experimental and Numerical Investigation of Hard Rock Breakage by Indenter Impact

Jiang

Cai

Wang

et al. 2020

Shock and Vibration

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To investigate the effect of indenter shape, impact energy, and impact velocity on the rock breakage performance, a test device for rock fragmentation by indenter impact was developed to obtain the rock breakage volume, depth, and area under different impact conditions. By comparing the rock breakage volume, depth, area, and specific energy consumption, the results show that indenter shape has a greater influence on the rock breakage performance than that of the impact velocity with the same impact energy, and impact energy plays a decisive role in rock breakage performance with an identical indenter shape and impact velocity. For the lowest to highest specific energy consumption, the order of indenter shape is cusp-conical, warhead, hemispherical, spherical-arc, and flat-top under the same impact energy and velocity, but the cusp-conical indenter is damaged after several impacts. The rock breakage volume, depth, and area all increase with the increase in impact energy, but the effect of the impact velocity could be ignored under the same impact energy. In addition, the rock breakage features of the numerical simulation and experiments are similar, which show that the crushing zone close to the indenter impact point is mainly caused by the high compressive stress, and then radial cracks are caused by the accumulative energy release. The findings of this study will contribute to progress in the performance and efficiency for percussive rock drilling.

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Section: Rock Breakage Performance Under Different Indentermentioning

confidence: 99%

Experimental and Numerical Investigation of Hard Rock Breakage by Indenter Impact

Jiang

Cai

Wang

et al. 2020

Shock and Vibration

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“…Traditional surrounding rock classification methods, such as rock mass rating (RMR), tunneling quality index (Q-system), and basic quality index (BQ) systems, have difficulty guiding TBM construction effectively [13]. Therefore, some methods have been proposed for surrounding rock classification and performance prediction for TBM tunneling, such as the Colorado School of Mines (CSM) model, the Norwegian University of Science and Technology (NTNU) model, the Q TBM model, the rock mass excavatability (RME) rating and classification system, and the robust classification method (RCM) [14][15][16][17][18]. All these methods have addressed the problem that traditional surrounding rock classification methods cannot cope with TBM construction to a certain extent, with better performances.…”

Section: Introductionmentioning

confidence: 99%

Study on the Classification and Identification Methods of Surrounding Rock Excavatability Based on the Rock-Breaking Performance of Tunnel Boring Machines

Zhang,

Shi,

Majiti

et al. 2023

Applied Sciences

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Rock mass conditions are extremely sensitive to tunnel boring machine (TBM) tunneling. Therefore, establishing a surrounding rock excavatability (SRE) classification system applicable to TBM tunnels. Accurately and intelligently identifying excavatability grades can also facilitate efficient TBM tunneling and intelligent construction. Specific excavation and penetration rates were used to evaluate SRE. Their correlations with geological and tunneling parameters were explored using the field data from two water conveyance tunnels in China with different lithologies. A high-precision empirical SRE classification system was constructed using TOPSIS for multi-objective decision-making, and it was verified using engineering cases. An intelligent identification model for SRE grades in the stable phase of a TBM excavation cycle was established using 12,382 TBM rock-breaking datasets and deep forest models. Ten characteristic parameters, e.g., total thrust, were selected as model input features. Hyperparameter optimization was achieved using the grid search method. Deep forest was compared with decision tree, random forest, support vector classifier, and deep neural network. The contribution of the model’s features was measured using random forest. The main conclusions are as follows: the proposed SRE classification method is feasible and matches well with the actual excavation. In the intelligent identification of SRE classification, the accuracy and F1 scores when using deep forest were 96.33% and 0.9581, respectively. Deep forest exhibited better grade identification performance than the four models. Among the ten input features, penetration is the most important feature for the model’s input, while the top shield cylinder rod’s chamber pressure is the least important. The findings can provide some references for SRE classification and prediction and intelligent TBM control.

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“…As an alternative, a model based on optimum energy has been proposed to select TBM tunnelling parameters in hard rock. The relationships among energy, geological conditions, and the TBM construction performance are analyzed by combining the results from the LCM tests and the on-site data [8]. However, due to a lack of sufficient input data, the accuracy of this approach seems relatively low.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Tunnelling Parameters for Underwater Shield Tunnels, Based on the GA-BPNN Method

Jiang

Gao³

et al. 2022

Sustainability

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Reasonable tunnelling parameters for underwater shield tunnels play an important role in maintaining driving efficiency and safety. In this paper, a neural network method was developed to predict tunnelling parameters. Soil properties and geometric parameters were taken as inputs for the neural network, which output the tunnelling parameters, such as advancing thrust, rotation, penetration, torque of the cutter head, and support pressure. In order to improve the stability of the neural network, a genetic algorithm (GA) with a global searching ability was used to optimize the initial weight of the neural network (GA-BPNN). The accuracy of the algorithm, based on GA-BPNN, was studied through an underwater shield tunnel project. The results showed that the integration of GA into the neural network significantly improves the prediction ability for shield tunnelling parameters, especially for adjustable parameters. Later, the developed GA-BPNN model was further utilized to predict and set the range of shield tunnelling parameters in fine sand stratum of high risk. Through a comparative analysis of tunnelling parameters, the reasons leading to ground instability have been found out, and the effectiveness of ground pre-reinforcement has been verified.

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A new method for selecting hard rock TBM tunnelling parameters using optimum energy: A case study

Cited by 44 publications

References 18 publications

Experimental and Numerical Investigation of Hard Rock Breakage by Indenter Impact

Experimental and Numerical Investigation of Hard Rock Breakage by Indenter Impact

Study on the Classification and Identification Methods of Surrounding Rock Excavatability Based on the Rock-Breaking Performance of Tunnel Boring Machines

Prediction of Tunnelling Parameters for Underwater Shield Tunnels, Based on the GA-BPNN Method

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