Outstanding issues in excavation of deep and long rock tunnels: a case study
Bibliographic record
Abstract
Excavation of deep and long tunnels faces several distinctive challenges such as unknown geological structures, high groundwater pressure, and high in situ stress, as compared with conventional tunnels. The deep and long Taining tunnel in Fujian Province, South China, was excavated in complex geological settings. This tunnel had to pass through various squeezing fault zones and intensely jointed zones. Large deformations and high-pressure groundwater were frequently encountered during excavation. To predict the potential adverse geological structures, a comprehensive method, which included tunnel seismic prediction and ground penetration radar detection as well as horizontal drilling, was developed. Energy release and pressure reduction, as well as timely sealing, pre-excavation curtain grouting, and radial grouting were adopted to control the high-pressure groundwater. Countermeasures including improvement of support stiffness, double-layered primary support, and pre-support, increase of preset deformation, grouting reinforcement, and timely installation of permanent lining were taken to ensure safe construction in the surrounding rock masses under high in situ stress.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".