Examining the Impact of Micro Silica Gel Additive on the Compressive Strength and Water Absorption of Roller Compacted Concrete Pavement
Bibliographic record
Abstract
Roller compacted concrete pavement (RCCP) is one of the different types of concrete pavements which is considered as a new developing technology due to its rapid installation. However, RCCP is difficult to install in high thickness; therefore, it is essential to reduce the thickness of pavement while maintaining strength. Flexural strength and fatigue resistance are the most important parameters effective on design of thick pavements. These parameters are directly related to uniaxial compressive strength of concrete. Hence, this study determines and evaluates the compressive strength of 7-day and 28-day specimens. Given the durability of concrete pavements, particularly their penetrability against water, corrosive materials and minerals, the most important parameter is to reduce water absorption of RCCP. In order to increase strength and reduce water absorption of RCCP, different additives as well as a proper mix design can be significantly effective. This study examines the effect of various mix designs and different percentages of micro silica gel on RCCP. The suggested mix design is continuous aggregation and addition of 7% micro silica gel, which increases strength and durability of RCCP.
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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".