2021
DOI: 10.1177/1687814021998831
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Research on rock breaking mechanism and load characteristics of TBM cutter based on discrete element method

Abstract: Tunnel boring machine (TBM) is a large-scale tunnel engineering equipment, which has unparalleled advantages in safety and work efficiency. The cutter is subjected to complex and variable random impact loads, resulting in damage to bearings, cutter rings, and cutter shafts. Therefore, based on the discrete element simulation platform and experiment, this paper established the cutter rock simulation model based on the experimental data, and analyzed the rock breaking process, cutter load magnitude, and variatio… Show more

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Cited by 8 publications
(3 citation statements)
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“…This method is called sliding window method, where the number of steps in the past time is called window width t. The width of the sliding window is the basis of prediction model. The five characteristic parameters total thrust, cutterhead torque, shoe support pressure, cutterhead rotate speed, and advance speed are expressed as x (1) , x (2) , x (3) , x (4) , x (5) , respectively. Taking the total thrust x (1) as an example, when the sequence is x (1) 1 , x (1) 2 , Á Á Á , x (1) 34560 n o , assume the training window width t = 5, and the training samples for the LSTM network predictor f are formed as follows:…”
Section: Surrounding Rock Class Prediction Model Based On Lstm-svmmentioning
confidence: 99%
See 1 more Smart Citation
“…This method is called sliding window method, where the number of steps in the past time is called window width t. The width of the sliding window is the basis of prediction model. The five characteristic parameters total thrust, cutterhead torque, shoe support pressure, cutterhead rotate speed, and advance speed are expressed as x (1) , x (2) , x (3) , x (4) , x (5) , respectively. Taking the total thrust x (1) as an example, when the sequence is x (1) 1 , x (1) 2 , Á Á Á , x (1) 34560 n o , assume the training window width t = 5, and the training samples for the LSTM network predictor f are formed as follows:…”
Section: Surrounding Rock Class Prediction Model Based On Lstm-svmmentioning
confidence: 99%
“…TBM is a kind of large-scale digital tunneling equipment, which is widely used in subways, water conservancy, highways, railways tunnel construction. [1][2][3] In the process of TBM construction, the type of surrounding rock is the key index of surrounding rock stability evaluation and tunneling performance prediction. 4,5 Predicting the surrounding rock classes accurately within a short distance is very helpful for the workers to formulate corresponding supporting measures in time.…”
Section: Introductionmentioning
confidence: 99%
“…Yang et al 19 In accordance with the construction requirements and geological conditions of TBM, a hybrid fuzzy comprehensive evaluation method was employed, utilizing both qualitative and quantitative indicators to select a cutter holder that meets the design specifications. Entacher et al [20][21][22][23] Load and vibration analyses were conducted on the cutting tools and tool holders, assisting in the TBM design and extending the lifespan of both the cutting tools and their holders. Huo et al 24,25 A dynamic model of the TBM cutting tool system and a predictive model for tool loads were established.…”
Section: Author Research Contentsmentioning
confidence: 99%