To study the mechanical properties of concrete containing cement and sand with biomaterial and glass waste in concrete
Bibliographic record
Abstract
This study looks into using local materials to improve concrete by replacing some of its traditional components. Specifically, it examines how rice husk ash (RHA) can partially replace pozzolanic Portland cement (PPC) and how glass powder waste can substitute sand. The goal is to find the best combination that strengthens RCC beams for concrete grades M35 and M40. This research also tackles the issue of managing agricultural waste, such as rice husks, and addresses the growing concern over glass waste, which is increasing due to high production rates and slow decomposition. By reducing cement use, the goal is to minimize the environmental impact, as cement production uses a lot of raw materials and releases harmful gases. The primary focus is on determining the optimal amount of glass powder waste that can replace sand in concrete, as it has excellent pozzolanic properties. Additionally, the study looks at how RHA, a local biodegradable material, can replace part of the PPC. Ultimately, this project seeks to find effective solutions for managing agricultural and glass waste while promoting more sustainable construction practices.
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 | 0.000 | 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) | 0.002 | 0.001 |
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".