Effect of High-Velocity Impact Loading on Concrete Slabs Reinforced by Metallic Strips from Soft Drink Cans as Fiber
Bibliographic record
Abstract
The idea of enhancing concrete slabs' ability to resist impact has been discussed in this study and, specifically, the use of soft drink can strips fibers (SDCSF).Concrete structures are classified as brittle material when happen to be exposed to impact loads like the firing of guns.Some of the past research works revealed that subjected to impact loads, the concrete structures disintegrated into several pieces.And because of the brittleness, fibers are incorporated into concrete at various ratios of 0. 5%, 1%, and 1.5% mean weight of cement and various proportions of 3cm, 6cm, and 9 cm to enhance concrete's resistance.Squire slab specimens with the dimension of (500 mm × 500 mm) and thickness of 50 mm.Ten specimens were subjected to high-velocity impact load by gunfire from a weapon M16, bullet diameter 7.62 mm, from a distance of 15 m.The arrangement used was to impact a single point (one bullet) at the centre of each panel.The research found that using such a type of fiber with different percentages and lengths could increase the resistance impact load.Moreover, the results showed that reducing the spalling area, the scabbing area, and reducing the redial crack length.It can be concluded that the spalling area of specimens with 1.5% soft drink can strips and 9 cm length at 28 days decreased by 21.48% compared to the reference sample (R).The scanning area of specimens with 1.5% soft drink can strips and 9cm length at 28 days was decreased by 24.72% compared to the reference sample (R).The redial crack length of specimens with 1.5% soft drink can strips and 9cm length at 28 days was decreased by 29.32% compared to the reference sample (R).
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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".