UHPFRC for concrete repair
Bibliographic record
Abstract
As part of the St. Lawrence Seaway lock maintenance, the current practice is to perform concrete repairs entirely with reinforced concrete, using either ordinary concrete or high performance concrete (HPC) mixtures. However, with the recent advances in the field of ultra-high performance fiber-reinforced concrete (UHPFRC), the use of this new material is considered in view of improving the overall performance of repairs. The goal is to implement repairs capable of dissipating a lot of energy before breaking when a ship hits a concrete lock wall. Numerous rehabilitation materials and methods have been experimented in the past. They all were unsuccessful due to inadequate shear and impact strength characteristics of the repair materials used. These needs can be efficiently fulfilled with UHPFRC, with their superior mechanical properties and very high energy-dissipation ability. To analyze the in-situ behavior of UHPFRC, two main mixture designs were investigated: a 160-MPa mixture containing 3% of steel fibers and a 120-MPa mixture containing 3.5% of a steel fiber blend. Thick repairs with average depths of 700 mm were carried out during the winter shut down period, in very harsh climatic conditions (-12 °C, gusty wind). The performance exhibited by the repairs after a full year shows that UHPFRCs can withstand very effectively the impacts from the transiting vessels
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.058 | 0.035 |
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".