Investigation on optimal lightweight expanded clay aggregate concrete at high temperature based on deep neural network
Bibliographic record
Abstract
Abstract This study explores the effects of mixture design parameters on the residual mechanical properties of lightweight expanded clay aggregate (LECA) concrete exposed to elevated temperatures. A total of 30 lightweight concrete mixtures were cast and exposed to three elevated temperatures, namely 250°C, 500°C, and 700°C. The test variables comprised the LECA percentage used as partial volume substitution for natural sand (0%, 25%, 50%, 75%, and 100%), silica fume partial replacement for cement by weight (5%, 7.5%, 10%, 12.5%, and 15%), cement content (300, 400, 500, 600, and 700 kg/m3), and different water‐to‐cement (W/C) ratios (0.25, 0.313, 0.375, 0.438, and 0.5). The compressive and indirect tensile strengths were measured before and after exposure to elevated temperatures. The results indicate that the lightweight concretes incorporating higher contents of cement and silica fume and made with lower W/C ratio exhibited higher initial mechanical properties yet incurred more significant drop in mechanical properties after exposure to 500°C and 750°C.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".