Microstructure of Structural Lightweight Concrete Incorporating Coconut Shell as a Partial Replacement of Brick Aggregate and Its Influence on Compressive Strength
Bibliographic record
Abstract
In this study, coconut shell aggregate (CSA) was used in brick aggregate concrete (BAC) to produce structural lightweight concrete. Various BACs containing CSA (CSBACs) were prepared based on the volumetric mix ratio of 1:1.5:3 (cement:fine aggregate:coarse aggregate). CSA was used substituting 0−15% of brick aggregate (BA) by weight. The concrete mixes were designed based on the weight-based water to cement (w/c) ratios of 0.45, 0.50, and 0.55. All the freshly mixed concretes were tested for their workability with respect to slump. In addition, the freshly mixed concretes made with the w/c ratio of 0.50 were examined for their wet density and air content. The hardened concretes were tested for their dry density, compressive strength, and microstructural characteristics (e.g., microcrack, micropore, fissure). The microstructure of CSBACs was investigated by a scanning electron microscope (SEM). In addition, the fissure width between the cement paste and CSA was measured from the SEM images using “ImageJ” software. The correlation between the compressive strength and fissure width of CSBAC was also examined. Test results showed that the air content of CSBACs including 5–15% CSA was higher than that of the control concrete (0% CSA). In addition, the density and compressive strength of concrete decreased with the increased CSA content. Above all, the most interesting finding of this study was the presence of fissures in the interfacial transition zone between the cement paste and CSA of CSBAC. The fissure width gradually increased with the increase in CSA content and thus decreased the compressive strength of concrete. However, the fissure width decreased with the increased curing age of concrete and therefore the compressive strength of CSBAC was enhanced at later ages. Moreover, a good correlation between the compressive strength and fissure width of CSBAC was observed in this study.
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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) | 0.000 | 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 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".