Bibliographic record
Abstract
As critical concrete infrastructure deteriorates, engineers need efficient and reliable techniques to appraise the causes and the extent of deterioration, to evaluate the structural consequences and to select effective management protocols and rehabilitation strategies. This book looks at deterioration caused by internal swelling reaction (ISR) mechanisms in concrete, such as alkali-aggregate reaction, delayed ettringite formation and freeze-thaw cycles. The book provides accessible and comprehensive coverage of recent work and developments on the most common ISR mechanisms leading to induced expansion and deterioration. It addresses the implications of ISR on different scales (micro, meso and macro), outlines qualitative and quantitative techniques to assess the condition of affected concrete and introduces the multi-level assessment protocol, using advanced microscopic and mechanical techniques, particularly the stiffness damage test and damage rating index, as a reliable approach to appraise ISR-affected infrastructure. Also included is a detailed case study of the Robert-Bourassa Charest Overpass in Quebec. Internal Swelling Reactions in Concrete: Mechanisms and Condition Assessment is primarily intended for undergraduate and graduate students, as well as academics interested in the field of concrete durability and condition assessment of concrete. It will also be of interest to engineers and infrastructure owners dealing with ISR-related problems.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".