A Dynamic Rock Mass Classification Method for TBM Tunnel

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

doi:10.1007/978-981-13-0113-1_34

Cited by 4 publications

(3 citation statements)

References 9 publications

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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%

“…[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. Moreover, the workers can conduct operation adjustment and preparation in time according to the judgment of geological conditions ahead.…”

Section: Introductionmentioning

confidence: 99%

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Prediction method of TBM tunnel surrounding rock classification based on LSTM-SVM

Liu,

Yang,

2024

Advances in Mechanical Engineering

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TBM tunnel surrounding rock classification is a key indicator for supporting decision-making and ensuring safe construction. And predicting the surrounding rock type accurately in advance is of great significance for TBM intelligent construction. This paper established the surrounding rock classification model based on support vector machine (LIBSVM), including preprocesses historical tunneling parameters, extracts data information that can accurately reflect the relationship between rock and machine, analyzes the correlation between different parameters and surrounding rock categories, and obtains highly relevant parameters. Based on the long short-term memory (LSTM), the prediction model of total thrust, cutter head torque, gripper pressure, cutter head rotate speed, and propulsion speed are established, which is the strongly correlated parameters with surrounding rock. Combining the parameter prediction model with the surrounding rock classification algorithm, the LSTM-SVM tunnel surrounding rock classification prediction model is established. The results showed that the coefficient of determination of the total thrust model, the cutter head torque, the gripper pressure, the cutter head speed, and the propulsion speed were 0.9825, 0.9396, 0.9974, 0.9843, and 0.9636. The overall prediction accuracy of the surrounding rock category can reach 86.0686%, which can provide a certain reference for predicting the surrounding rock condition in a short distance.

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Section: Surrounding Rock Class Prediction Model Based On Lstm-svmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction method of TBM tunnel surrounding rock classification based on LSTM-SVM

Liu,

Yang,

2024

Advances in Mechanical Engineering

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show abstract

“…It is also known that PR is positively correlated with thrust at the same rock mass class. However, under the traditional BQ rock mass classification method, no clear correlation between PR and thrust was found in the same rock class [42]. Therefore, the conventional BQ rock mass classification method still needs to be improved for the SRE classification of TBM tunnels.…”

Section: Surrounding Rock Excavatability Classification Based On Rock...mentioning

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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Grouped machine learning methods for predicting rock mass parameters in a tunnel boring machine‐driven tunnel based on fuzzy C‐means clustering

Wang,

Ni,

Zhang

et al. 2024

Deep Underground Science and Engineering

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To guarantee safe and efficient tunneling of a tunnel boring machine (TBM), rapid and accurate judgment of the rock mass condition is essential. Based on fuzzy C‐means clustering, this paper proposes a grouped machine learning method for predicting rock mass parameters. An elaborate data set on field rock mass is collected, which also matches field TBM tunneling. Meanwhile, target stratum samples are divided into several clusters by fuzzy C‐means clustering, and multiple submodels are trained by samples in different clusters with the input of pretreated TBM tunneling data and the output of rock mass parameter data. Each testing sample or newly encountered tunneling condition can be predicted by multiple submodels with the weight of the membership degree of the sample to each cluster. The proposed method has been realized by 100 training samples and verified by 30 testing samples collected from the C1 part of the Pearl Delta water resources allocation project. The average percentage error of uniaxial compressive strength and joint frequency (Jf) of the 30 testing samples predicted by the pure back propagation (BP) neural network is 13.62% and 12.38%, while that predicted by the BP neural network combined with fuzzy C‐means is 7.66% and 6.40%, respectively. In addition, by combining fuzzy C‐means clustering, the prediction accuracies of support vector regression and random forest are also improved to different degrees, which demonstrates that fuzzy C‐means clustering is helpful for improving the prediction accuracy of machine learning and thus has good applicability. Accordingly, the proposed method is valuable for predicting rock mass parameters during TBM tunneling.

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A Dynamic Rock Mass Classification Method for TBM Tunnel

Cited by 4 publications

References 9 publications

Prediction method of TBM tunnel surrounding rock classification based on LSTM-SVM

Prediction method of TBM tunnel surrounding rock classification based on LSTM-SVM

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

Grouped machine learning methods for predicting rock mass parameters in a tunnel boring machine‐driven tunnel based on fuzzy C‐means clustering

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