Use of porous lightweight aggregate in high performance concrete as a carrier of chemical admixtures and curing water
Bibliographic record
Abstract
Internal curing of concrete can be achieved by soaking porous lightweight aggregate (LWA) in water before its introduction into the concrete mix as a partial replacement for normal density aggregate. This technique is particularly useful for low water-cement ratio concrete, for which self-desiccation can lead to autogenous shrinkage, tensile stresses and cracking at early ages. A research project has been undertaken to develop low-shrinkage high performance concrete for the design of concrete structures with long service life. One specific objective was to optimize the concrete mix design by introducing selected chemical admixtures into the concrete mix by using porous lightweight aggregate as a carrier. Expanded shale lightweight aggregate sand was soaked in a solution of water and given admixtures, such as a shrinkage-reducing admixture (SRA) and/or a corrosion inhibitor (CI), prior to mixing. Several fresh and hardened concrete properties were measured and compared to those of a similar concrete mix, in which the given chemical admixtures were added directly into the mix according to the manufacturer?s specifications. The results showed that this new admixture delivery method produced no adverse effects on the desired fresh and hardened concrete properties, including compressive strength and autogenous shrinkage. The addition of SRA through LWA mitigated chemical interactions between the air entraining admixture and the SRA, which was previously found to reduce the effectiveness of the air entraining admixture. For instance, when SRA was delivered through LWA, it was found that the target air content of 5% could be achieved with 10 times less air entraining admixture.
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 | 0.000 | 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) | 0.001 | 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 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".