The Damage Rating Index Method for ASR Affected Concrete—A Critical Review of Petrographic Features of Deterioration and Evaluation Criteria
Bibliographic record
Abstract
Abstract The Damage Rating Index method has recently been used with success in several cases of damage evaluation in structures affected by alkali-silica reaction in Canada and in Brazil. Although this petrographic method is starting to be widely used and is in the process of becoming integrated as a Canadian standard, it has not been modified yet from the original design. An evaluation of the method is presented in this paper. According to data obtained from many petrographic examinations, the number of cracks in coarse aggregates (filled or not with silica gel) seemed to show to best correlation with the expansion measured on laboratory concrete specimens made with Spratt limestone. The reaction rim is not a real “damage” feature and should not be considered as one but as a “degree of reaction” feature. In an attempt to improve the DRI method for assessing damage related to ASR, a new parameter should be introduced, which takes into account cracks running from aggregate particles to cement paste. The geological nature of the rock used as concrete aggregate may influence the reaction mechanism as well as the petrographic features related to ASR. Comparing concrete specimens subjected to ASR, which incorporate different aggregate types may, in some instances, be influenced by the type of reaction produced by the various reactive rocks and minerals in each aggregate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".