Thermal performance of self-compacting concrete: destructive and nondestructive evaluation
Bibliographic record
Abstract
Recently, self-compacting concrete (SCC) has been increasingly used in high-rise buildings and industrial units, susceptible to accidental fires. The probable degradation of these structures necessitates understanding SCC behavior under elevated temperatures. For this, an extensive experimental investigation was undertaken to evaluate the effect of elevated temperature (300–600 °C) on the mechanical compressive properties of SCC; considering the effect of water-to-cement ratio (0.40–0.50), type of mineral aggregate and filler (limestone and basalt), and internal humidity. Standard cylinder (150 mm × 300 mm) and prism (100 mm × 100 mm × 300 mm) specimens were prepared from various SCC mixtures, cured for 28 d in limewater, and then stored at different environments for an additional 90 d to create varying internal humidity levels; ranging from 28 to 95%. Later, specimens were subjected to elevated temperatures in an electrical furnace, then cooled and tested for compressive mechanical response or non-destructively using resonance frequency, ultrasonic pulse velocity, and rebound hammer evaluation techniques. The results showed significant reduction in residual compressive strength, and elastic modulus, and an increase in compressive strain at peak stress and toughness as elevated temperature was increased. The SCC mixtures at upper water-to-cement ratios with basalt aggregate showed higher resistance to elevated temperatures than corresponding ones with limestone. Internal humidity in SCC had a detrimental impact on compressive strength and elastic modulus; especially at exposure temperatures below 400 °C. The statistical correlations between residuals for compressive strength or elastic modulus and nondestructive damage indices can be classified as very good. Furthermore, the nonlinear empirical models, developed to predict residuals for compressive strength and elastic modulus in terms of the study parameters, showed relatively high prediction potential, hence are recommended to be used in designing SCC mixtures for best resistance against possible fire attack.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".