EVALUASI BAUT PADA SISTEM SAMBUNGAN WOOD PLASTIC COMPOSITE (WPC) JATI DENGAN VARIASI KUAT PENGENCANG METODE GESER DUA IRISAN BERDASARKAN NDS 2018
Bibliographic record
Abstract
Wood Plastic Composite (WPC) merupakan salah satu produk pemanfaatan limbah serbuk kayu dan polimer plastik HDPE (High Density Polyethlene) yang dibentuk melalui sistem ekstrusi. Penggunaan WPC sebagai bahan komponen struktural memiliki keterbatasan pada ukuran. Oleh karena itu diperlukan sambungan baut. Penelitian ini dilakukan untuk mengetahui nilai kapasitas sambungan WPC menggunakan alat sambung baut. Proses pemasangan baut menggunakan torque wrench, untuk mengetahui kekencangan yang tepat. Penelitian ini menggunakan metode geser dua irisan. Menggunakan baut standar dengan diameter 10 mm. Tahanan lateral pengujian didapat dari 5% Offset diameter. Penelitian dilakukan di laboratorium Bahan Bangunan, Jurusan Pendidikan Teknik Sipil dan Perencanaan Fakultas Teknik Universitas Negeri Yogyakarta. Berdasarkan hasil pengujian didapatkan nilai tahanan lateral WPC jati dengan nilai pengencang 6 Nm, 9 Nm, 12 Nm dan15 Nm diperoleh angka berturut-turut sebesar 15182,2 N; 17348,8 N; 15725,6 N; 19563 N. Nilai tahanan lateral pada variasi kekencangan 12 Nm mengalami penurunan dikarenakan baut terlalu kuat dan kaku menyebabkan WPC mengalami kegagalan terlebih dahulu sehingga sistem sambungan tidak bekerja dengan baik. Variasi kekencangan 15 Nm mendapatkan nilai tahanan lateral terbesar dikarenakan semakin besar kekencangannya semakin besar tahanan lateralnya.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".