Use of Innovative Pre-Cast Concrete Slab Repair Technology in Canada
Bibliographic record
Abstract
\In 2004, the Ministry of Transportation Ontario (MTO) carried out a trial project to evaluate construction techniques for pre-cast concrete slab repairs in concrete pavement. The trial was carried out on Highway 427, a heavily trafficked freeway in Toronto, Canada. The trial project required demonstrations of three pre-cast concrete pavement full-depth repair methods: the Fort Miller Super-SlabTM Intermittent Method, the Fort Miller Super-SlabTM Continuous Method, and the Michigan Method. Each method involves designing and fabricating pre-cast concrete slabs to replace deteriorated concrete pavement. The methods differ in how the base is prepared and how the pre-cast slab is installed and dowelled to the existing concrete pavement. Non-destructive testing using a Falling Weight Deflectometer (FWD) was undertaken after construction to assess load transfer efficiency (LTE) and to detect loss of support underneath the pre-cast slab. Details of the methodologies, site conditions, contract specifications, construction and FWD analysis are presented. This is the first construction experience in Canada with innovative pre-cast concrete slab repairs for concrete pavements. MTO will continue to monitor the field performance of these technologies and assess the cost effectiveness of this alternative to full-depth fast-track concrete repairs.
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 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".