Numerical modeling of temperature profiles in hardening belitic calcium sulfoaluminate cement-based mortars for permafrost region applications
Bibliographic record
Abstract
Belitic calcium sulfoaluminate (BCSA) cement-based mixtures are suitable for permafrost region applications due to their fast strength development. To better understand their performance and guide future applications, this study aimed to develop a numerical model for predicting the temperature profiles in BCSA cement-based mixtures used in permafrost regions. Isothermal calorimetry and the Arrhenius equation were used to determine the heat generation rate of BCSA cement. The modeling of temperature profiles in BCSA cement-based mixtures was implemented with a finite element model, which was validated with experimental results. In the model, the temperature profiles in BCSA cement-based samples cured in cold sand (0 °C, −5 °C and −10 °C) to mimic the influence of permafrost environments, and another group was cured in cold air at the same temperatures. The results indicate that the developed numerical model can accurately predict the temperature profiles in hardening BCSA cement-based mixtures for permafrost region applications (root mean square error ≤ 1.3 °C). The modeling results provided valuable guidance to future research and practical applications of BCSA cement-based mixtures in permafrost regions. First, samples should be cured in cold soil or sand when investigating the performance of BCSA cement-based materials used in permafrost regions because the temperature in samples cured in sand was notably (e.g. 28 °C) lower than that of the same mixture cured in air. Second, precautions are needed to control thermal cracks when BCSA cement-based mixtures are used in cold temperatures because the temperature gradient (e.g. 45 °C) in the BCSA cement-based sample was high even if the sample size was small (e.g. Ø300 × 600mm).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".