The Study of the Effects of Supplementary Cementitious Materials (SCMs) on Concrete Compressive Strength at High Temperatures Using Artificial Neural Network Model
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Bibliographic record
Abstract
In this study, an artificial neural network (ANN) model was developed to predict the compressive strength of concrete containing supplementary cementitious materials (SCMs) at high temperatures. For this purpose, 500 experimental results were collected from the available literature. The effective parameters in the model are the volumes of coarse and fine aggregates, water, cement, coarse-aggregate type, percentage SCMs as the cement replacement, temperature levels, and test methods. The proposed ANN model was developed at a correlation coefficient of 0.966. A parametric study was conducted to evaluate the impact of the combined effects of input parameters (aggregate types and SCM content) on the relative compressive strength of concrete at high temperatures. It was shown that siliceous aggregate has a better performance by producing stronger bonds with cement paste than calcareous aggregates. The optimum SCM contents depend on the aggregate types. The optimum silica fume (SF) content for concrete with a water-to-binder ratio of 0.6 subjected to high temperatures is 8% and 3% for siliceous and calcareous concrete, respectively. The analysis of the ANN model has provided a conclusive understanding of the concrete behaviour at high temperatures.
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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