ANALISIS PRODUKTIVITAS METODE PELAKSANAAN PENGECORAN BETON READY MIX PADA BALOK DAN PELAT LANTAI GEDUNG
Bibliographic record
Abstract
Abstract: Implementation method technology of multi-storey building concrete construction is experiencing significant growth, both in material processing and casting equipment. Several casting equipment including concrete lift and concrete pump have different productivities which contribute to time and cost. This research aims to analyze the productivity of casting equipment, time and cost required, as well as the break-even point of casting method of ready mix concrete application on the beams and the floor slabs of buildings, particularly on the second- floor, third- floor and forth-floor using concrete lift and concrete pump. Data was obtained by conducting interviews and observations concerning casting implementation of building construction projects that use K-300 ready- mix concrete. Regression and Correlation analysis are used to obtain time and cost comparison between both method of casting implementation, as well as Break Even Point analysis to obtain breakeven point of casting volume with regards to cost and time. The analysis showed that casting productivities using lift on first, second and third floor are 7,166 m3/h, 5,945 m3/h, 5,125 m3/h, while the productivities using concrete pump on first, second and third floor are 36 m3/h, 30 m3/h , 24 m3/hour. Cost comparison of 1 m3 increment of casting using concrete lift and concrete pump is Rp. 99 330: Rp.19.000 (5.23: 1), while time ratio is 8.272 minutes: 2,172 minutes (3.8: 1). Breakeven point analysis towards casting cost showed that the second floor which has volume greater than 95.89 m3, using concrete pump method is more optimal than concrete-lift.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".