Investigation on Mechanical and Durability Properties of Slurry Infiltrated Fiber Concrete (SIFCON)
Bibliographic record
Abstract
ABSTRACT The construction industry is in need of finding cost effective materials for increasing the strength of concrete structures. Slurry infiltrated fibrous concrete (SIFCON) is one of the recently developed construction materials that can be considered as a special type of high-performance fiber reinforced concrete (HPFRC) with higher fiber content. An endeavor has been made in the present investigations to study the influence of addition of locally available material at different dosages to the total weight of concrete. An experimental program was carried out to investigate the compressive strength, flexural strength, Non-Destructive Test (NDT), such as rebound hammer test and ultra-sonic pulse velocity test, and durability test such as sulfate attack. The investigations were done using cement-based mix and tests were carried out as per recommended procedures by appropriate codes. SIFCON specimens with 0%, 5%, 10% and 15% volume of fraction fibers and with aspect ratio (L/D) 20, 25 and 30were used in this study. Test results were presented in comparison of SIFCON with conventional plain concrete. The load carrying capacity of SIFCON specimen is found to be higher than conventional plain concrete and it also reduced crack width. SIFCON was found to have many differences from conventional plain concrete and fiber reinforced concrete. Regarding its mix constitute material, fresh SIFCON properties and hard SIFCON properties. Therefore, special standard test methods and compliance criteria should be prepared for this material.
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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".