Bibliographic record
Abstract
In severe chloride/sulfate exposure conditions, there is need to provide more durable concrete, but this usually requires use of concretes with high cementitious binder contents that increase the risk of cracking due to thermal, autogenous, and drying shrinkage. Cracking is counter-productive to obtaining durability in the field. High performance concrete (HPC) can be durable in aggressive environments due to its superior mechanical, resistance to fluid transport, and durability characteristics. However, HPC mixtures typically have high binder contents (cement, pozzolanic minerals), and low w/cm, and therefore undergo high heat of hydration as well as autogenous shrinkage, resulting in rapid volume changes. The increased risk of autogenous deformation in high performance concrete can increase the risk of the cracking and durability of reinforced concrete structures such as bridge decks. For example, in Ontario the MTO had issues with in-creased incidence of transverse cracking in bridge decks when 50 MPa HPC was used relative to traditional 35 MPa concretes. A review of their construction records indicated that this problem was often related to concretes that set quickly and rapidly developed high temperature rise due to heat of hydration. The effects of ground granulated blast-furnace slag (GGBFS) and silica fume (SF) on the durability of HPC were investigated. In this study, HPC mixtures at 0.33 w/c were made with three sources of blended cements containing 8% SF mixed with 25, 35 and 50% GGBFS replacements by mass of cement. The compressive strength, drying shrinkage, and transport properties were measured. The test results have shown that increased fineness of the silica fume blended cement enhances the resistance to fluid transport and mechanical properties, but results in increased drying shrinkage, leading to increased cracking potential. A comprehensive study was conducted to understand (a) the interrelation between different mechanisms of volume change (thermal deformation, autogenous shrinkage, drying shrinkage) under different standard and non-standard conditions and (b) the role of different shrinkage mitigation methods (e.g. by either increasing GGBFS content to overcome early-age free and re-strained thermal deformation as well as autogenous shrinkage, or by reducing the cement binder content).
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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) | 0.002 | 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".