A5000990018- Tunneling and Rock Mechanics
- Drilling and Well Engineering
- Mineral Processing and Grinding
- Rock Mechanics and Modeling
- Image Processing and 3D Reconstruction
- Grouting, Rheology, and Soil Mechanics
- Advanced Decision-Making Techniques
- Geoscience and Mining Technology
- Infrastructure Maintenance and Monitoring
- Geophysical Methods and Applications
- Geotechnical Engineering and Analysis
- Atomic and Subatomic Physics Research
- Geotechnical and Geomechanical Engineering
China Institute of Water Resources and Hydropower Research
2022-2025
This review summarizes the research outcomes and findings documented in 45 journal papers using a shared tunnel boring machine (TBM) dataset for performance prediction efficiency optimization learning methods. The big was collected during Yinsong water diversion project construction China, covering excavation of 20 km-section with 199 items monitoring metrics taken an interval one second. were result call contributions TBM contest 2019 covered variety topics related to intelligent TBM....
This paper addresses the significance of preprocessing big data collected during a tunnel boring machine (TBM) excavation before it is used for learning on various TBM performance predictions. The research work based two water diversion tunneling projects that cover 29.52 km and 17 051 cycles. It has been found penetration rate calculated from raw measured distances exhibits more random behavior owing to their percussive vibratory cutterhead. A moving average method process negative...
This review discusses the application scenarios of machine learning-supported performance prediction and optimization efficiency tunnel boring machines (TBMs). The rock mass quality ratings, which are based on Chinese code for geological survey, were used to provide "labels" suitable supervised learning. As a result, generation grades reasonably agreed with ground truth documented in maps. In contrast, main operational parameters, i.e., thrust torque, can be predicted historical data....
Tunnel boring machines (TBMs) accumulate vast operational data crucial for analyzing complex rock‐machine interactions during construction. Recent advancements in big mining and machine learning (ML) have spurred significant artificial intelligence (AI) research tunnel The effectiveness of ML models depends on high‐quality tunneling data, prompting increased emphasis preprocessing. This review synthesizes current practices preprocessing construction, covering characteristics, methods...
During tunnel construction with earth pressure balance (EPB) shield machine, the machine operators determine attitude correction parameters depending only on their own experiences. Inappropriate parameter setting may lead to more and deviation of equipment attitude, delay period, or even ground collapse. An method for an EPB is proposed in this study assist new determining suitable operating advance considering previous experience. The first reconstructs tunneling according experienced...
During the excavation process of Tunnel Boring Machines (TBM), a large amount data is collected in real time. It has become increasingly important to assess quality surrounding rock by indirectly understanding parameters. Based on this vast dataset from sites, study proposes novel Torque Penetration Index (TPI), which better suited for evaluating state during TBM excavation. The TPI was validated through statistical analysis on-site experimental and dataset. research findings indicate that...
The full-face tunnel boring machine (TBM) is operated by the driver and essential for excavation. However, previous studies have considered impact of driver's preset values on excavation performance. To address this gap, study utilized historical TBM data from multiple engineering projects to investigate relationship between response values. We introduced principles obtaining analyzed their correlation. results revealed that development trend cutterhead speed showed good consistency. In...