Marshall Characteristics of Quicklime and Portland Composite Cement (PCC) as Fillers in Asphalt Concrete Binder Course (AC-BC) Mixture
Bibliographic record
Abstract
Quicklime and Portland Composite Cement (PCC) were used in a concrete-asphalt mixture (C-AM) combination to compare quicklime and Portland Composite Cement (PCC).The Marshall characteristics test was used to discover the distinguishing characteristics of a hot asphalt mixture made of quicklime and Portland composite cement (PCC) to make asphalt-type Asphalt Concrete Binder Course (AC-BC).This study used petroleum bitumen with a 60/70 penetration and bitumen contents of 5.0%, 5.5%, 6.0%, 6.5%, and 7.0% created by combining quicklime and cement in a filler.In this study, quicklime and cement were used as fillers; both materials have been used to create concrete buildings and have demonstrated their strength.To determine values for stability and density, Marshall tests were run.The Marshall technique of 2 x 75 blows was used in this study to get the necessary road materials, which were obtained in the composition of asphalt concrete binder course, in accordance with the Indonesia 2018 General Specification and the Indonesia Requirement (in Indonesian).The results showed that the filler quicklime at the optimum bitumen content (OBC) 6.5% had stability Marshall 873.9 kg, VIM 3.9%, VFB 76.63%, flow 3:33 mm, Marshall Quetiont 262.16 kg/mm, and VMA 16,49%, whereas the filler cement had stability Marshall 905.5 kg, VIM 4.1%, and VFB 76.57%, Hot mix asphalt with cement filler works better than quicklime filler.for building using intermediate layers (asphalt-concrete binder course).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".