Bridge rehabilitation detailing manual (BRDM) : (Commentary on MTO structure rehabilitation manual)
Bibliographic record
Abstract
The Ministry of Transportation of Ontario (MTO) has developed few manuals that deal with inspection and rehabilitation of bridges; such as OSIM (Ontario Structural Inspection Manual), SRM (Structure Rehabilitation Manual), OPSS (Ontario Provincial Standards Specifications) and OPSD (Ontario Provincial Standards Drawings). Most of these manuals except OPSD do not provide detailed sketches to clarify the theory; therefore, practicing engineers in rehabilitation industry have difficulties during design and construction of rehabilitation projects. The main objective of this project is to provide a manual called "Bridge Rehabilitation Detailing Manual (BRDM)" for clarifying practicing engineers' problems. This manual presents the results of a wide research and studies on rehabilitation of about 80 bridges in Ontario. Most of these bridges are located at major locations of Ontario with high volume of traffic going over. Out of the 80 bridges, 62 bridges have been considered as case-studies since they were designed and detailed by top ranked and well-known consulting firms in rehabilitation industry. The most common deterioration and relevant rehabilitation methods were categorized in a database to help the development of the BRDM. This has been combined with MTO's manuals to result and act as commentary of MTO Rehabilitation Manual.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.005 |
| Insufficient payload (model declined to judge) | 0.046 | 0.022 |
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".