Experimental Study on Macro-performance of Long-age HydraulicConcrete Based on High Temperature Accelerated Curing
Bibliographic record
Abstract
In view of time-consuming and expensive for the long-age mechanical property test of hydraulic concrete, the change rule for the mechanical properties of hydraulic concrete with long-age is still unclear. Based on the equivalent age theory, the high-temperature curing method was employed to accelerate test. First, the macro-mechanical properties tests of hydraulic concrete at different curing ages (90d, 180d, 1a, 2a, 3a) and different fly ash contents (0%, 15%, 35%) were designed and carried out. Then the change rule of mechanical properties of hydraulic concrete at long-age were analyzed. In addition, the macro test results of concrete core specimen of a gravity dam which has been operated more than 40 years were adopted to feedback the above test results. The research results showed that the fly ash content had a significant influence on the activation energy Ea of hydraulic concrete. To reach the same hydration degree of design long-age, the curing time increased with the increasing of fly ash content. Within the curing age of 3a, the compressive and splitting tensile strength of concrete increased with the increasing of curing age. The strength values of cement concrete and concrete with 15% fly ash content were close to each other, while the strength values of concrete with 35% fly ash content were smaller than the cement concrete and concrete with 15% fly ash content. The consistency and reliability of the rule that the concrete strength continues to increase with age was further verified by combining the macroscopic test results and the strength growth rate calculation results of a gravity dam concrete core specimen that had been in service for more than 40 years.
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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 | 0.000 |
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".