Improving mechanical properties of concrete by using fibrous materials
Bibliographic record
Abstract
Concrete is the most significant source of construction in the construction industry of the world. However, concrete causes excessive production of cement, which is one of the key contributors of carbon dioxide emissions to the environment. To minimize the use of cement in concrete, various innovative materials are being added in concrete to make it sustainable. In this study, a comparative study between three fibers Carbon Fiber, Glass Fiber, and steel fibers was done to determine which one is the most suitable fiber. For this purpose, testing on specimens was done for tensile and compressive strength at 0.5% addition of each fiber. Testing was done after 3, 7, 14, and 28 days of curing. The results spelled out that the highest compressive strength of 37.53 MPa of cube specimens was found in carbon fiber after 28 days, and Glass fibers exhibited the lowest gain in strength at about 32.335 MPa. Carbon Fiber gained 28% more strength than the control mix. On the other hand, tensile strength was also found highest in carbon fibers i.e. for the cubes the maximum difference between different fibers inducted concrete samples is 28% approximately, and for cylinders it is 27%, respectively. On the other hand, the highest tensile strength of concrete was also gained with the carbon fiber at about 3.61 MPa. The same was found lowest in glass fiber at 3.12 MPa. Carbon fiber got about 44 % improvements in tensile strength.
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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) | 0.001 | 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".