Experimental Study on Early-Age Crack of Mass Concrete under the Controlled Temperature History
Bibliographic record
Abstract
Thermal deformation under restrained conditions often leads to early-age cracking and durability problems in mass concrete structures. It is crucial to monitor accurately the evolution of temperature and thermal stresses. In this paper, experimental studies using temperature stress testing machine (TSTM) are carried out to monitor the generated thermal cracking in mass concrete. Firstly, components and working principle of TSTM were introduced. Cracking temperatures and stress reserves are selected as the main cracking evaluation indicators of TSTM. Furthermore, effects of temperature controlling measures on concrete cracking were quantitatively studied, which consider the concrete placing temperature (before cooling) and cooling rates (after cooling). Moreover, the influence of reinforcement on early-age cracking has been quantitatively analyzed using the TSTM. The experimental results indicate that the crack probability of reinforced concrete (RC) is overestimated. Theoretical calculations proved that the internal stress can transfer from concrete to reinforcement due to creep effect. Finally, the experimental results indicate that the reinforcement can improve the crack resistance of concrete by nearly 30% in the TSTM tests, and the ultimate tensile strain of RC is approximately 105% higher than that of plain concrete with the same mix proportions.
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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 | 0.001 |
| 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".