Kuat Tekan Beton Mutu 21,7 MPa Berdasarkan SNI 7656:2012 dan AHSP Tahun 2022
Bibliographic record
Abstract
Concrete is a mixture of portland cement or hydraulic cement, fine aggregate, coarse aggregate and water with or without additives, may contain other cementitious materials and other chemical additives, forming a solid, strong, and stable mass. The purpose of the study was to determine the compressive strength of concrete with a quality of fc' 21.7 MPa with a quality mixing method based on SNI 7656:2012 and with a mixing method based on the 2022 Unit Price Analysis (AHSP). The analytical method used in this study is an experimental method conducted at the Laboratory of the Faculty of Engineering, Batanghari University. The results obtained several conclusions, namely (1) Based on the results of the compressive strength test of concrete, the compressive strength value at the age of 7,14,28 days using the SNI 7656:2012 method has increased. At the age of 28 days, the obtained characteristics meet the quality requirements (design compressive strength). The average compressive strength of concrete at the age of 7.14.28 days was obtained as follows: 18.16 MPa (SNI 7 Days), 20.62 MPa (SNI 14 Days), 22.14 MPa (SNI 28 Days). (2) Based on the results of the compressive strength test of concrete, the value of the compressive strength at the age of 7,14,28 days using the 2022 AHSP method has increased. At 28 days the nuts obtained meet the characteristics quality requirements (design compressive strength). The average compressive strength of concrete at the age of 7.14.28 days was obtained as follows: 24.83 MPa (AHSP 7 Days), 30.34 MPa (AHSP 14 Days), 25.09 MPa (AHSP 28 Days). (3) The mix design uses the reference standard of SNI 7656:2012 and AHSP 2022. Planning is carried out using research results that have been obtained from testing concrete stacking materials and the provisions stipulated by SNI 7656:2012 and AHSP 2022.
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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) | 0.001 | 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 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".