Experimental and RSM-based optimization of sustainable concrete properties using glass powder and rubber fine aggregates as partial replacements
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 it