Enhancing the Strength Properties of Self-Compacting Concrete with Fiber Reinforcement
Bibliographic record
Abstract
Self-compacting concrete is one that is flow able by its own. The SCC is suitable for placing in dense reinforcement structures. It is a new generation performance concrete known for its outstanding deformity and high resistance to bleeding. The concrete is frail material which is comparatively tough in compression but fragile in tension. The tensile strength of concrete is improved by addition of fibers in the concrete mix. The addition of such fibers has negative consequence on the workability of concrete. Various types of fibers are used in concrete to provide the higher flexural strength and better tensile strength. In this research steel fibers are used to provide a better strength as compared with normal reinforced concrete. Steel fiber in SCC significantly improves its flexural strength, improved tensile properties, reduce cracking and improve durability. In this research the investigation of steel fiber in SCC to enhance the strength properties of SCC. The objective of the study was to determine different properties of SCC with steel fiber at different proportions. The experimental investigation was took on the freshly mixed and hardened properties of SCC of various mix with the different variations of fiber 0.25%, 0.50%, 0.75% and 1% by using Viscosity Modified Agent (VMA) 1.5% of cement material by using M25 grade of concrete. In this research a series of tests were carried out for workability like slump cone test, U funnel, V funnel, L box test on SCC to check freshly mix properties like flow-ability, filling-ability, and passing-ability and hardened properties like compressive strength, split-tensile strength and flexural strength respectively and test were conducted at the age of 7Days, 14Days, 28Days on the SCC. The advantage of adding steel fiber in self-compacting concrete is that it enhances its overall 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".