The Toronto Transit Commission's Subway Tunnel and Station Leak Remediation Grouting Program
Bibliographic record
Abstract
The Toronto Transit Commission (TTC), one of the largest public transportation systems in North America has been plagued by leaking tunnels since the time they were constructed. The water infiltration problems was causing both delays and concerns for passenger safety as well as causing accelerated aging of the rail and rail fastening systems, deterioration and malfunction of electrical systems and their components, and decay of the structure itself. A professionally engineered state-of-the-art solution grouting program has brought the problem under control. The grouting program at the TTC, which was started up in May 1997, is one of the largest continuous on-going leak remediation grouting projects using solution grouts in North America. The leakage remediation program originally focused on solving the tunnel leakage problems but has since expanded to include water infiltration problems in the stations. The success gained to date can be attributed to a combination of several key components: the assembly of an in-house team of grouting expertise—from design engineer to field technician and the selection and meticulous use of the most suitable sealing materials available in industry for this specific and extremely difficult application. The unique challenge of performing all the leak remediation grouting work within the nightly two hour maintenance window without impacting on customer service was successfully accomplished using a strategically implemented, engineered grouting procedure. Time limitations and difficult ground conditions proved to be the two most difficult obstacles facing the grouting engineer. The grouting design had to take into account the multiple phase, multiple stage grouting, operations that were anticipated and required to successfully shut off the leakage problems.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.005 |
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".