Response surface methodology approach for optimized compressive strength of some mix design concrete aggregates from waste cockle shells and glass powder
Bibliographic record
Abstract
Nowadays, with increased demand for aggregates for concrete and an awareness of the need of protecting natural resources, experts are becoming increasingly interested in waste material as a building material substitute. However, the compressive strength is influenced by the composition of concrete. In this study, the compressive strength of concrete under substitution using waste from cockle shells and glass was investigated using Response Surface Methodology (RSM). Central Composite Design (CCD) based on RSM was used to assess the influence of epoxy resin, cockle shells powder, and glass powder on compressive strength responses. RSM developed first-order and second-order mathematical models with findings from experimental design. Analysis of variance was used to determine the correctness of CCD's mathematical models. Desirability analysis was then employed to optimize epoxy resin, cockle shells powder, and glass powder yielding maximum compressive strength. The RSM analysis revealed that the empirical results fit well into linear and quadratic models of concrete compressive strength. The mixing components will produce cement with compressive strength in each formulation of 54.71 MPa (4.88% epoxy resin and 4.0% cockle shells powder), 47.82 MPa (6.85% epoxy resin and 8.0% glass powder), 147.0 MPa, (4% cockle shells powder and 8% glass powder), and 56.08 MPa (4.4% epoxy resin, 4.0% cockle shells powder, and 8.0% glass powder). The results confirmed that a reasonable compressive strength of concrete could be achieved using epoxy resin, cockle shells powder, and glass powder.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".