Tool wear analysis of pressurized face TBM drives in the glacial geology of the Pacific Northwest
Bibliographic record
Abstract
Abstract For underground construction projects in the United States and Canada it is standard procedure to use a Geotechnical Baseline Report (GBR) to contractually define subsoil conditions. The GBR sets baselines based on which tunneling contractors develop bids and plan the works. Baseline values for soil abrasiveness are a focus especially where drives with pressurized‐face Tunnel Boring Machines (TBM) beneath the groundwater table and in unstable face conditions require changing the cutterhead tools under hyperbaric conditions or in pre‐constructed safe havens. Several laboratory procedures exist that can be used for providing soil abrasiveness baselines in the context of the GBR. However, none of them cover all the soil characteristics that are relevant in causing tool wear. Also, other factors need to be considered for wear rate prediction. Analyzing the performance of previous TBM drives is a proven way to gain insight into the wear system behavior. This paper presents correlation analyses of geotechnical conditions, TBM operational data, and tool wear measurements from several TBM drives in the metropolitan areas of Seattle and Vancouver, B.C. These drives with earth pressure balance and slurry TBMs include various tool types and were conducted in glacial and interglacial deposits that are considered highly abrasive.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".