Investigation of moisture condition and Autoclam sensitivity on air permeability measurements for both normal concrete and high performance concrete
Bibliographic record
Abstract
While on site measurement of air permeability provides a useful approach for assessing the likely long term durability of concrete structures, no existing test method is capable of effectively determining the relative permeability of high performance concrete (HPC). Lack of instrument sensitivity and the influence of concrete moisture are proposed as two key reasons for this phenomenon. With limited systematic research carried out in this area to date, the aim if this study was to investigate the influence of instrument sensitivity and moisture condition on air permeability measurements for both normal concrete and HPC. To achieve a range of moisture conditions, samples were dried initially for between one and 5 weeks and then sealed in polythene sheeting and stored in an oven at 50 °C to internally distribute moisture evenly. Moisture distribution was determined throughout using relative humidity probe and electrical resistance measurements. Concrete air permeability was subsequently measured using standardised air permeability (Autoclam) and water penetration (BS EN: 12390-8) tests to assess differences between the HPCs tested in this study. It was found that for both normal and high performance concrete, the influence of moisture on Autoclam air permeability results could be eliminated by pre-drying (50 ± 1 °C, RH 35%) specimens for 3 weeks. While drying for 5 weeks alone was found not to result in uniform internal moisture distributions, this state was achieved by exposing specimens to a further 3 weeks of sealed pre-conditioning at 50 ± 1 °C. While the Autoclam test was not able to accurately identify relative HPC quality due to low sensitivity at associated performance levels, an effective preconditioning procedure to obtain reliable air permeability of HPC concretes was identified.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".