Des structures en beton a haute performance sans fissures
Bibliographic record
Abstract
High-performance concretes necessitate special care during both placing and curing; they do not bleed and are thus highly sensitive to plastic shrinkage. Should this material not be cured properly (within a few hours), it may develop considerable autogenous shrinkage while its tensile strength remains very low. Building durable structures using high-performance concrete requires limiting plastic shrinkage as well as autogenous shrinkage over the first several hours following the placing of the concrete. Nonetheless, it is critical to allow for a residual autogenous shrinkage to take place after a few days of water curing, such that menisci form inside the hydrated cement paste in order to reduce permeability, absorptivity and consequently the penetration of aggressive agents. The City of Montreal uses high-performance air-entrained concretes, with water/cement ratios varying between 0.35 and 0.37 and a corresponding 28-day characteristic strength of 60 and 50 MPa, in the construction of nearly all its civil engineering works, protective barriers and sidewalks, which are exposed to rather harsh environmental conditions. These structures have been used to generate a standard set of detailed specifications applicable to water curing.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".