Flexural failure of ultra-high performance concrete subjected to the alternating cryogenic and elevated temperature via acoustic emission characterization
Bibliographic record
Abstract
This paper aims to reveal the mechanical evolution and potential mechanism of Ultra-high Performance Concrete (UHPC) exposed to a complex temperature-variation environment typical of Liquefied Natural Gas storage tank. Herein, the effect of steel fiber geometries (straight and hooked-end) on flexural failure of UHPCs in the range of −170 °C ∼ 200 °C was studied, and dynamic fracture evolution during the loading process was tracked using acoustic emission (AE) test. Results indicated that the flexural strength of concrete specimen enhanced at −170 °C but diminished at 200 °C in the first cycle. For multiple cycles, its improved strength subsisted until the third cycle, after which gradually decreased. Moreover, the hooked-end fiber generally had superior strengthening and toughening effects than straight fiber, except for cryogenic temperature and third cycles tests. At cryogenic temperature , the freezing of pore moisture inside specimen enhanced the matrix strength and the interfacial bonding between the matrix and fibers. At elevated temperature, the thawing, diffusion and evaporation of moisture in concrete could induce matrix dehydration and weaken the bonding effect between interfaces. In varying exposure environments, the moisture migration would also undergo secondary hydration reactions with unreacted cement particles. Finally, using AE parameters to effectively record damage progression in UHPCs exposed to extreme temperatures was also highlighted.
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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".