Investigating the Effect of Applying Fish Scale Powder in Concrete as Sustainable Blending Materials
Bibliographic record
Abstract
This study investigates the mechanical, microstructural, and economic performance of concrete incorporating calcined fish scale powder (FSP) as a partial replacement for Portland cement.Fish scales, an abundant organic waste byproduct, were thermally treated at 250℃ and added to concrete mixes at 0%, 0.5%, 1%, 1.5%, 5%, 10%, and 15% replacement ratios.Compressive, splitting tensile, and flexural strengths were evaluated at 3, 7, 28, and 90 curing days to capture early and long-term performance trends.The highest mechanical performance was recorded at a 10% replacement level, achieving compressive, tensile, and flexural strengths of 42 MPa, 2.3 MPa, and 4.3 MPa respectively at 90 days, representing significant improvements over the control.SEM analysis at 7 and 28 days confirmed substantial microstructural densification due to enhanced C-S-H gel development and better interfacial bonding from the hydroxyapatite-collagen composite structure of FSP.Workability peaked at 1.5% FSP content, with higher dosages negatively impacting flowability.Furthermore, a costbenefit evaluation revealed that the 10% FSP mix offers not only improved mechanical performance but also material cost savings and reduced CO₂ emissions.These findings highlight the potential of fish scale powder as a sustainable cement substitute that enhances structural performance and environmental efficiency, supporting its application in green construction practices.
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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) | 0.001 | 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".