Optimization of Hollow Core Slab Strength Based on SFRC Orientation
Bibliographic record
Abstract
The construction industry has been broadly developed and improved over the last decades.One of the functional innovations in this sector is the employment of steel fibers.This work assesses the impacts of using Steel Fiber Reinforced Concrete (SFRC) on the properties of hollow-core slabs.The hollow core slab (HCS) is considered in this research.Also, this study explored the influence of utilizing some SFRC fractions on the characteristics of hollow-core slabs.Four case studies were addressed, including (A) Conventional concrete, (B) Concrete with Type 1 SFRC, (C) Concrete with Type 2 of SFRC with a ratio of 0.5%, and (D) Concrete with Type 2 with a portion of 1%.ANSYS software package was used to guide numerical analysis and modeling of HCS and identify major parameters that affect the HCS performance with the help of Finite Element Analysis (FEA).Depending on the numerical results, it was found that using SFRC between 0.5% and 1% in concrete tubes tested numerically provided significant rates of durability, minimized deflection, and enhanced the mechanical behavior of concrete.Furthermore, the work outcomes confirmed that optimum displacement was attained when the SFRC ratio was 1.5%, corresponding to load values of 25 kN to 200 kN.Besides, the findings affirmed that using the first type of SFRC accomplished a considerable decline in the deflection of concrete.Deflection reduction ratios of 4.16% and 6.23% were obtained after adding the first and second types of SFRC into the reinforced hollow concrete core, respectively.Meanwhile, adding the first and second types of SFRP into non-reinforced hollow concrete core accomplished a reduction portion of 12.2% and 20.39%, respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".