Evolution of thermal parameters of wet‐screened dam concrete after different freeze–thaw deterioration
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Bibliographic record
Abstract
Abstract Even with specialized equipment, it is difficult to measure the thermal parameters of dam concrete during freeze–thaw testing. In addition, numerical calculations usually assume that freeze–thaw cycling does not change the thermal parameters of the aggregate and mortar of dam concrete, which makes it difficult to accurately determine how these parameters evolve during freeze–thaw deterioration. To address this challenge, we propose herein a new method to determine the thermal parameters of wet‐screened dam concrete during freeze–thaw deterioration. This method involves freeze–thaw testing, temperature perturbation, numerical calculation, and optimization inversion. First, 200 rapid freeze–thaw tests and 5 temperature perturbation tests of 5 concrete specimens were carried out. Next, orthogonal design parameters were used to calculate the temperature field of the specimens via the finite‐element method. A neural network was then established based on the difference between the measured and calculated temperatures. The expected difference and measured density were fed into the model to invert the thermal parameters of the concrete for different water–cement ratios, ages, and content of the air‐entraining agent. The results show that, upon increasing the number of freeze–thaw cycles, the thermal conductivity of wet‐screened dam concrete gradually decreases, the specific heat remains relatively constant, and the surface heat‐transfer coefficient gradually increases.
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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 | 0.000 | 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) | 0.000 | 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 it