Experimental and RSM-based optimization of sustainable concrete properties using glass powder and rubber fine aggregates as partial replacements
Bibliographic record
Abstract
Abstract To promote sustainability in concrete production, this study investigates the combined use of glass powder (GP) and rubber fine aggregates (RF) as partial replacements for cement and natural fine aggregates (NF), respectively. The study aligns with several Sustainable Development Goals (SDGs). Ten mixtures were developed using Central Composite Design (CCD) within the Response Surface Methodology (RSM) framework, with GP and RF replacement levels ranging from 0 % to 35 %. Replacing cement with 15 % GP improved compressive strength, tensile strength, and stiffness due to pozzolanic reactivity and packing effects, while higher levels (25–35 %) reduced performance because of increased water demand and dilution. RF replacement up to 15 % maintained workability and strength; beyond this, mechanical properties declined due to RF’s low specific gravity (1.06 g/cm 3 ), weak bonding, and higher porosity. The optimal mix, GP15RF15, achieved a slump of 92 mm, 28-day compressive strength of 40.1 MPa, tensile strength of 5.3 MPa, and modulus of elasticity of 25,914.5 MPa, comparable to the control mix. Correlation analysis showed strong positive relationships among compressive strength, tensile strength, and stiffness ( r ≥ 0.99), while RF content had strong negative correlations ( r = −0.75 to −0.77). Optimization using the desirability function yielded a score of 1.000, with prediction errors below 1.35 %. The results confirm the viability of GP–RF concrete as a durable and eco-efficient alternative for non-prestressed structural components and general infrastructure.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".