Field and laboratory investigations on condition assessment of ASR-affected structures
Why this work is in the frame
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Bibliographic record
Abstract
This paper aims at providing a critical analysis of the applicability of engineering tools for the condition assessment of ASR-affected structures, focusing on visual quantification of external and internal cracking and moisture condition measurements. The main objective of the presented investigations was to demonstrate how such engineering tools can provide important data regarding condition of the concrete and, in turn, help the selection of proper maintenance strategies. The paper analyses data on the extent of reaction/damage in concrete components incorporating different reactive aggregates and alkali content, and with varying exposure conditions (moisture access). A surface cracking mapping method was applied during the in-situ inspections. In the laboratory, the internal damage and moisture condition were assessed on extracted cores, primarily by use of the Damage Rating Index (DRI) and Degree of Capillary Saturation (DCS). The results highlight the challenges of investigating ASR in affected structures and the importance of using suitable tools, as the external and internal conditions of the concrete can vary significantly in the same structure or component, despite similar exposure conditions. The potential causes for such variability include the concrete composition, the aggregate reactivity, the reinforcement detailing and other forms of movement restraints and the loading conditions. The condition assessment tools used throughout this study usually allowed to better document/explain different scenarios observed during several field investigations. The influence of moisture access upon ASR damage generation was also clearly illustrated in several case studies. In general, external and internal signs of damage tend to correlate well with one another. However, internal condition can significantly vary over the depth of an investigated component and this feature should be considered when diagnosing ASR.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it