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Updated: Jun 11, 2026
12:18
Pore-scale Imaging and Characterization of Hydrocarbon Reservoir Rock Wettability at Subsurface Conditions Using X-ray Microtomography
Published on: October 21, 2018
AI and knowledge driven computation of rock mass characteristic parameters across engineering projects
Zewang Zheng1, Yunpei Zhang2, Quan Xu3
1College of Civil Engineering, Zhejiang University of Technology, Hangzhou, 310014, Zhejiang, China.
Scientific Reports
|June 9, 2026
Summary
This study introduces an AI-driven method to predict rock mass quality for tunnel boring machines (TBMs). The approach enhances construction safety and efficiency by accurately identifying rock characteristics using novel parameters and machine learning.
Area of Science:
- Geotechnical Engineering and Artificial Intelligence
- Underground Construction and Tunnelling Technology
- Machine Learning Applications in Civil Engineering
Background:
- Accurate prediction of rock mass quality ahead of Tunnel Boring Machines (TBMs) is crucial for underground construction efficiency and safety.
- Existing AI/ML methods face challenges with massive, noisy TBM data, feature selection, and limited data from new projects.
- Current physics-based and fitting-based methods have limitations in stability, adaptability, and generalizability across diverse geological and engineering conditions.
Purpose of the Study:
- To propose an AI- and knowledge-driven method for computing reliable rock mass characteristic parameters.
- To address limitations in existing methods for predicting rock mass quality using TBM data.
- To develop a robust TBM-based rock mass quality prediction model applicable across multiple projects.
Main Methods:
- Developed a rock-breaking data filtering method based on effective disc cutter energy conversion.
- Identified high-efficiency rock-breaking stages using data from three diverse TBM projects (YC, YS, HB).
- Computed knowledge-driven rock mass characteristic parameters (a, b, Torque Penetration Index - TPI) as input features.
- Employed a CatBoost AI model for rock mass class prediction, tested across different projects.
Main Results:
- The proposed characteristic parameters demonstrated a strong correlation with actual rock mass quality.
- Achieved high prediction accuracies: 85.60% (YC), 86.48% (YS), and 88.89% (HB), outperforming conventional methods.
- The AI-driven approach showed superior performance and robustness in multi-project scenarios.
Conclusions:
- The AI- and knowledge-driven method effectively computes rock mass characteristic parameters for improved prediction accuracy.
- This approach offers new technical support for cross-project data utilization and real-time rock mass quality prediction.
- The findings have significant implications for enhancing construction safety and efficiency in new tunnel projects.
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