Design Methodology to Evaluate Hydraulic Jacking in Pressure Tunnels
Bibliographic record
Abstract
This paper presents methodology and tools for use in evaluation of potential hydraulic jacking in the concrete-lined section of a pressurized headrace tunnel immediately adjacent to a steel-lined tunnel section. The locations of potential in situ stress deficiency in the rock mass are uncertain but could be located in the transition section. Therefore, the performance of reinforced concrete-lining in the transition zone and elsewhere are evaluated for hydraulic jacking. The evaluation of hydraulic jacking was performed by using finite element method (FEM) and discrete element method (DEM). The computer programs ABAQUS and UDEC were used for FEM and DEM analyses, respectively. In addition to the analytical approach, a review of the available approaches focusing on the performance of concrete-lined pressure tunnels was performed. The response of the tunnel system including the lining and surrounding rock mass was evaluated for various scenarios. The evaluation includes: (1) extent of hydrojacking; (2) exfiltration from the tunnels; and (3) structural stability of the tunnel system. A series of sensitivity analyses were performed using numerical modeling to parametrically evaluate the influence of rock mass joint variations on the results.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".