Dry-cast concrete masonry products: properties and durabilityThis article is one of a selection of papers published in this Special Issue on Masonry.
Bibliographic record
Abstract
Performance of dry-cast concrete masonry products (DCCMPs), which are becoming the product of choice for many applications, has yet to be assessed in a comprehensive manner. This study was undertaken to investigate the effects of mix design and manufacturing parameters on the mechanical and transport properties, as well as the freeze–thaw (F/T) durabilities, of DCCMPs. The variables studied were water to cement ratio, mixing time, vibration time, and curing regime. Freeze–thaw durability was assessed in accordance with the American Society for Testing and Materials (ASTM) standard C1262 by exposing specimens to four conditions: water, 3% NaCl, 4% CaCl2, and 4% MgCl2 solutions. Results revealed that 3% NaCl exposure yielded the most mass loss due to scaling, whereas 4% MgCl2 exposure exhibited the least. Increase in either water content, vibration time, mixing time, or use of moist curing led to improvement in the mechanical properties, refinement of large pores in the range of 40 to 400 μm, reduction of total porosity, and enhancement of F/T durability of capstones. Ionic sorptivity yielded a strong statistical correlation with mass loss due to F/T action in the presence of 3% NaCl and 4% CaCl2.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 0.001 |
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".