Prediction of alkali–silica reaction expansions of mortars containing glass waste
Bibliographic record
Abstract
The majority of existing findings regarding expansion (EXP) risks in concretes containing waste glass stem from experimental studies. There is a need for rapid assessment methods to ensure safer recycling of glass waste in cementitious composites. In this study, an artificial neural network (ANN) model was developed to accurately predict alkali–silica reaction EXP/mitigation resulting from the integration of glass waste in mortars. The analysis considered glass incorporation either separately as waste glass powder (WGP) and waste glass aggregates (WGAs), or in combination, at contents of up to 100% for WGA and 30% for WGP. A set of 175 mixtures was analysed, considering five distinct variables, which encompassed different mix proportions, involving varying components of cement, natural aggregates, WGP and WGA, in addition to the duration of environmental exposure. The results show that the EXP of WGA mortars decreased with the increased incorporation of WGP. The EXP values obtained from validation and experience confirm the high accuracy of the developed ANN model, with validation coefficients reaching up to 98.061% and a small value of the mean square error.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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 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".