Quantitative Petrographic Technique for Concrete Damage Due to ASR: Experimental and Application
Bibliographic record
Abstract
Abstract An automatic petrographic test procedure for quantifying damage in concrete affected by alkali-silica reaction (ASR) is presented in this paper. A computer image analysis program was designed to quantify the degree of microcracking and the amount of silica gel resulting from ASR. The procedure involves the petrographic examination at a magnification of ×20 of polished concrete sections impregnated with fluorescent epoxy resin for cracking and uranyl acetate coated sections for the determination of their silica gel content. Also, the data were compared to the results obtained from a semi-quantitative petrographic method, i.e., the Damage Rating Index commonly used in Canada for evaluating the condition of concrete affected by ASR. The petrographic examination was first carried out on laboratory sections cut and polished from concrete prisms incorporating two different aggregate types, the Spratt limestone and the Potsdam sandstone. Both methods were also applied to specimens prepared from a core collected from a large dam affected by ASR. Good correlation was obtained between analytical parameters derived from the image analysis method and the expansion levels of the laboratory test prisms; however, no explicit relation was found to date between the amount of gel as measured in this study and the expansion level. This study shows that the quantitative petrographic method using the image analysis and the Damage Rating Index Method can be used to estimate the condition and current expansion of concrete specimens cored from concrete structures affected by ASR.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".