Engineering-Based FE Approach to Appraise Slender Structures Affected by Alkali-Aggregate Reaction (AAR)
Bibliographic record
Abstract
Alkali-aggregate reaction (AAR) is one of the most harmful distress mechanisms affecting the performance of aging reinforced concrete structures worldwide. Although several prediction models have been developed to assess the chemical reaction, a thorough and comprehensive approach with the capabilities to correlate important parameters that affect AAR and the mechanical properties of deteriorated materials, as well as the abilities to describe the current damaged state of AAR-affected structures (diagnosis) and predict the potential of further damage (prognosis) is still lacking. Such information is essential in selecting efficient remedial/rehabilitation actions for existing structures in the field. This project aims to develop a practical, yet accurate engineering-based finite element (FE) model for assessing AAR damage and predicting the future behaviour of affected infrastructure. The model is validated through three analyses. First, its capability to accurately simulate sound concrete under mechanical loading is verified by successfully simulating different beam failure mechanisms and cracking patterns, as well as predicting the members’ full force-deflection curves. Next, AAR anisotropic expansion under different stress-state (confinement) conditions is accurately simulated and verified by correlation with laboratory tests. Lastly, an AAR-affected slender reinforced concrete structure (Robert-Bourassa/Charest overpass) is successfully simulated by performing a condition assessment based on several tests performed prior to its demolition.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".