Use of pore solution analysis in design for concrete durability
Bibliographic record
Abstract
Pore solution analysis of hardened cement-based materials has been in increasing use over the last 30 years to determine the ionic composition of the pore fluid at various ages and after exposure to various temperature regimes. In addition, it has been used to evaluate the potential for results of chemical interaction with external solutions or with other concrete materials (such as alkali-reactive aggregates). Once related to physical durability test data, knowledge of the pore solution composition in combination with the nature of the solid phases present can be used to predict the potential for future durability-related chemical interactions. This paper discusses three areas where the usefulness of pore solution analysis has been instrumental in increasing the understanding of concrete durability issues related to: (a) alkali binding as it affects alkali–silica reaction (ASR), (b) chloride-binding as it affects reinforcement corrosion resistance, and (c) delayed ettringite formation (DEF).
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".