Production of Groundwater Resistance Mortar Using Glass Sand and Polypropylene Fibres
Bibliographic record
Abstract
Concrete structures that are submerged in water suffer from attacks by harmful salts and acids such as sulfates and chlorides.Therefore, using waste like polypropylene (PP) fiber to prevent environmental accumulation and glass sand (GS) (rich in silica) may help solve this problem.This study aims at the possibility of replacing 25, 50, and 75% of the fine aggregate with GS and using PP fibers at a rate of 1% of the total volume to produce sustainable mortar.The mechanical and physical properties, including compressive strength, flexural strength, splitting tensile strength, and density, were investigated.The water absorption was also monitored using indirect tests for an indication of the permeability of the mortar.The tests were evaluated on different curing ages (7, 14, 28, and 90 days) by two types of curing mediums: tap water and groundwater.The results indicated that at age 28 days, the specimens cured by groundwater and containing 25% and 50% of GS and 1% PP fiber improved the compressive strength by 7.5% and 7.4%, respectively, while the splitting tensile strength improved by 6.17% and 6.38%, and the flexural strength improved by 11.42% and 10.71%, respectively.In contrast, the specimens containing 75% GS and 1% fiber exhibited a clear reduction in compressive, splitting, and flexural strengths, reaching 7.7%, 6.36%, and 11.44%, respectively.However, the mean reduction of density and water absorption was 4.17% and 0.22%, respectively.The findings of this research introduced a comprehensive understanding of the groundwater-resistant mortar using GS and PP fibers.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".