Research on Rock-Breaking Characteristics of Cutters and Matching of Cutter Spacing and Penetration for Tunnel Boring Machine

The effectiveness of Earth Pressure Balance (EPB) Tunnel Boring Machines (TBMs) in urban underground construction relies on understanding and optimizing their performance under variable geotechnical conditions. This study investigates the key parameters impacting TBM efficiency during the construction of the Jakarta Mass Rapid Transit (MRT) Underground Section CP106. Data from TBM operation were analyzed using statistical and machine learning techniques, including Mutual Information (MI), Partial Dependence Plots (PDP), and Analysis of Variance (ANOVA), to identify influential parameters such as Tensile Strength, Uniaxial Strength, Spacing, and Penetration. Predictive models, including Gradient Boosting Regressor, Random Forest Regressor, and Linear Regression, were evaluated based on error metrics and R-squared values, with Gradient Boosting Regressor showing the highest predictive accuracy. Clustering analyses using K-Means and Principal Component Analysis (PCA) further classified operational states, identifying conditions that optimize energy efficiency and reduce mechanical wear. The findings suggest that TBM configurations with lower Specific Energy, Normal Force, and Rolling Force contribute to more efficient, less force-intensive tunneling. These insights provide a basis for refining TBM operations and predictive modeling in urban tunneling projects.

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Experiment Study on Rock Mass Classification Based on RCM-Equipped Sensors and Diversified Ensemble-Learning Model

Li,

Zeng,

et al. 2024

Sensors

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The geological condition monitoring and identification based on TBM-equipped sensors is of great significance for efficient and safe tunnel construction. Full-scale rotary cutting experiments are carried out using tunnel-boring machine disc cutters. Thrust, torque and vibration sensors are equipped on the rotary cutting machine (RCM). A stacking ensemble-learning model for real-time prediction of rock mass classification using features of mathematical statistics is proposed. Three signals, thrust, torque and a novel vibration spectrogram-based local amplification feature, are fed into the model and trained separately. The results show that the stacked ensemble-learning model has better accuracy and stability than any single model, showing a good application prospect in the rock mass classification.

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Research on Rock-Breaking Characteristics of Cutters and Matching of Cutter Spacing and Penetration for Tunnel Boring Machine

Cited by 3 publications

References 27 publications

Preparation of iron-based composites reinforced with submicron/nano-sized TiC ceramic particles by in-situ reaction in melts and one-step integrated hot pressing densification

Preparation of iron-based composites reinforced with submicron/nano-sized TiC ceramic particles by in-situ reaction in melts and one-step integrated hot pressing densification

Predictive Modeling and Optimization of TBM Operations: Advanced Techniques Applied to the Jakarta MRT Project

Experiment Study on Rock Mass Classification Based on RCM-Equipped Sensors and Diversified Ensemble-Learning Model

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