专题：Tunneling and Rock Mechanics

This cluster of papers focuses on the prediction of Tunnel Boring Machine (TBM) performance in hard rock conditions, utilizing rock mass properties, rock fragmentation, and machine learning techniques. It also includes research on risk assessment, numerical simulation, and the influence of geological conditions on TBM performance.
最新文献
Threshold vibration metrics of drilling tools as indicators of bit wear and rate of penetration decline: Field trials and data interpretation

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Intelligent control and optimization of shield tunneling machines in tunnel construction: Insights from excavation parameter data analysis and interpretable machine learning

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Lightweight diamond/WC–Co composites achieve synergistic hardness-toughness enhancement via high-pressure sintering

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Progress and challenges in high-vanadium high-speed steel for shield machine cutter tools

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A real-time prediction model for tunnel rock strength using geological drilling data and physics-informed neural networks

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Analysis and Optimization of Key Factors in Self-Supervised Lithology Recognition from TBM Muck Images

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Spatiotemporal-information-driven surrogate modeling for predictive analysis of shield tunneling-induced settlement

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Developing a calibrated physics-based digital twin for construction vehicles

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Tunnel Collapse Risk Analysis Based on Attribute Mathematical Theory and TSP Geological Forecast Technique

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Bayesian ensemble learning for rockburst grade prediction in high Geo-Stress TBM tunnels with SHAP-Based interpretability

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近5年高被引文献
Application of artificial intelligence in geotechnical engineering: A state-of-the-art review

review Full Text OpenAlex 310 FWCI43.4096

TBM Tunnelling in Jointed and Faulted Rock

book Full Text OpenAlex 238 FWCI0

Systematic review on tool breakage monitoring techniques in machining operations

article Full Text OpenAlex 198 FWCI17.8486

Prediction of ground surface settlement by shield tunneling using XGBoost and Bayesian Optimization

article Full Text OpenAlex 172 FWCI25.1081

Prediction of geological characteristics from shield operational parameters by integrating grid search and K-fold cross validation into stacking classification algorithm

article Full Text OpenAlex 171 FWCI23.5628

Automated Recognition Model of Geomechanical Information Based on Operational Data of Tunneling Boring Machines

article Full Text OpenAlex 162 FWCI18.4356

Comprehensive review of machine learning in geotechnical reliability analysis: Algorithms, applications and further challenges

article Full Text OpenAlex 156 FWCI32.0186

Hybrid semantic segmentation for tunnel lining cracks based on Swin Transformer and convolutional neural network

article Full Text OpenAlex 152 FWCI23.4231

Predicting tunnel squeezing using support vector machine optimized by whale optimization algorithm

article Full Text OpenAlex 147 FWCI16.2599

Investigation of feature contribution to shield tunneling-induced settlement using Shapley additive explanations method

article Full Text OpenAlex 145 FWCI20.6272