Engineering-based finite-element approach to appraise reinforced concrete structures affected by alkali–aggregate reaction
Bibliographic record
Abstract
Modelling the expansion and damage generated by alkali–aggregate reaction (AAR) in reinforced concrete structures is quite complex, yet necessary to obtain accurate predictions of the structural response of distressed members. Several AAR models have been developed to predict expansion and damage at the material (microscopic) or the structural (macroscopic) scales. However, those models tend to either neglect or overemphasise the critical physicochemical parameters of the reaction, which limits their applicability. Therefore, a new simple yet reliable finite-element approach is proposed to fill this gap. It accounts for the most important parameters affecting AAR through an engineering approach, without the need for non-technical guesses or to ‘fit’ model parameters. The proposed model is validated through the computational simulation of reinforced concrete specimens cast and monitored in the laboratory. Results show that AAR expansion was accurately simulated by accounting for the anisotropic (stress state dependent) nature of the reaction, mechanical properties deterioration and an analytical equation capable of representing AAR's free expansion. Next steps include validating the approach by simulating real structures and incorporating phenomena like leaching and combined distress mechanisms.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".