Design criteria for caverns under high stress rock conditions for Snowy 2.0 Power Station Complex
Bibliographic record
Abstract
Snowy 2.0 is a major hydro power project currently under construction in Australia. This new development aims to increase the capacity of the existing Snowy Mountains hydro power scheme by 2000 MW, making it the largest source of renewable energy in Australia and one of the largest pumped-storage projects in the world. The exceptional size of the Power Station Complex, situated at about 720m depth under significant in-situ stresses, in sedimentary rocks of fair to good resistance represented a major design challenge. This article presents the main features of the Snowy 2.0 Power Station Complex (PSC) project, in particular the geological context and the general layout of the PSC. It focuses on the methodology adopted for the definition of the design criteria, with the absence of relevant code or standard for the design of large deep underground caverns under high stress conditions. These criteria concern mainly (i) the stability analysis of the rockmass surrounding the excavation, including the stability of the rock pillars at the centre of the Power Station Complex; and (ii) the design of the rock support composed of fully grouted rockbolts, shotcrete and wiremesh.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".