Comparative analysis concerning the structural performance and resilience of concrete materials incorporating glass powder and conventional concrete
Bibliographic record
Abstract
The construction industry relies on a variety of structural materials, with concrete being a favored choice due to its outstanding strength and durability. Conventional concrete is typically made up of cement, fine aggregates, and crude aggregates, with its strength designed to meet specific requirements. Enhancing sustainability and minimizing environmental impact can be accomplished through utilizing waste substances in concrete production. One approach involves partially replacing cement and fine aggregates blended with waste glass powder. Additionally, superplasticizers serve as commonly utilized to lower the water-cement ratio (F/C), thereby improving the power properties of concrete. Effective curing remains a crucial factor in augmenting structural durability and concrete's mechanical characteristics in structures. The study examines strength and durability characteristics of concrete from integration using glass powder in place of certain cement and fine aggregate. Cement-based material mixtures are prepared with two different w/c values of 0.4 and 0.5 and replacement concentrations of 10%, 15%, and 20% for both the binder and fine aggregates. Essential parameters include fast chloride permeability and water absorption; comparative analyses are conducted to determine performance differences between conventional concrete and the modified version. The results of assessing the viability of adding leftover glass powder to concrete, this study aims to improve environmentally friendly building techniques.
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.
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 | 0.001 | 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) | 0.002 | 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".