KUAT TEKAN BETON POLIMER BERBAHAN ABU VULKANIK GUNUNG SINABUNG DAN RESIN EPOKSI
Bibliographic record
Abstract
Beton polimer merupakan suatu jenis beton yang terbuat dari bahan polimer sebagai binder penggati semenportland. Dibanding dengan semen portland, beton yang terbuat dari polimer relatif lebih baik dalam hal: kuat tekan, stabilitas volume, dan durabilitas.Pada penelitian ini, semen Portland tipe I merk Andalas sebagai binderdisubstitusi dengan kombinasi abu vulkanik Gunung Sinabung (AV) dan resin epoksi (RE) sebagai material polimer.Agregat kasar dan agregat halus dipakai berupa kerikil dan pasir sungai. Benda uji beton dicetak menggunakan silinder berukuran 150 mm x 300 mm untuk masing-masing variasi polimer sebanyak 5 sampel dan untuk beton normal 5 sampel. Adapun kombinasi variasi polimer yang digunakan adalah: (5% AV + 5% RE), (12% AV + 7% RE), dan(25% AV + 10% RE) dengan mutu beton normal rencana sebesar 17,50 MPa. Hasil penelitian memperlihatkan bahwa besarnya kuat tekan yang dihasilkan untuk masing-masing variasi di atas secara berutan adalah: 14,83 MPa, 22,53 MPa dan 25,36 MPa, sedangkan beton normal memiliki kuat tekan sebasar 18,74 MPa. Kombinasi variasi (12% AV + 7% RE) dan(25% AV + 10% RE) memberi hasil kuat tekan lebih baik dibanding beton normal, masing-masing meningkat sebesar 20,22% dan 35,32%. Hal ini menunjukkan bahwa peningkatan kuat tekan dapat dicapai dengan menggunakan kombinasi polimer lebih besar dari 5%.Kata kunci: kuat tekan,beton polimer, abu vulkanik, resin epoksi.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".