Extending service life of high performance concrete bridge decks with internal curing
Bibliographic record
Abstract
High performance concrete (HPC) bridge decks areprone to premature cracking if movement is restrained. Internal curing (IC) can reduce shrinkage cracking in concrete structures, thus improving their per-formance and service life. However, there is very limited information available in the literature on the possi-ble extension of service life due to internal curing. This paper addresses this question by using predictive models to estimate the service lives of typical bridge decks made with concrete using internal curing as op-posed to conventional curing. Four options are compared: (i) normal concrete deck; (ii) HPC deck; (iii) HPC deck with internal curing; and (iv) very high performance concrete deck with internal curing. It was found that the use of internal curing can increase the service life of HPC bridge decks by almost ten years, which is mainly due to due a slower penetration of chlorides as a result of reduced cracking.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".