Perbandingan Kapasitas Penggunaan Formula Dinamis Pada Tiang Pancang Sebagai Kontrol Daya Dukung
Bibliographic record
Abstract
Pondasi adalah bagian penting dalam suatu bangunan, dimana pondasi menjadi struktur bagian bawah (sub-structure) bagi setiap bangunan sipil, termasuk jembatan. Jembatan Krueng Cut ini dilewati oleh kendaraan berat, karena jembatan ini menghubungkan antara kawasan kota menuju ke pelabuhan Malahayati demikian juga sebaliknya. Ruang lingkup penulisan adalah melakukan perhitungan kapasitas daya dukung dinamis tiang pancang abutment 2 dengan menggunakan data pemancangan (calandering) di lapangan, dengan menggunakan sepuluh rumus dinamis masing-masing adalah metode Janbu, AASHTO, ENR Modified, Danish, Eytelwein, Gates, Navy-Mckay, Canadian National Building, Pasific Coast Uniform Building Code (PCUBC), dan Hiley, penggunaan perhitungan formula dinamis ini bertujuan untuk mengkontrol daya dukung yang telah dicapai tiang di lapangan. Dari penelitian ini disimpulkan bahwa penggunaan metode Janbu, AASHTO, ENR Modified, Danish, Eytelwein, Gates,dan Navy-Mckay memiliki resiko yang rendah apabila digunakan sebagai control daya dukung tiang dilapangan. Sedangkan penggunaan metode Canadian National Building, Pasific Coast Uniform Building Code (PCUBC), dan Hiley memiliki resiko yang tinggi. Dari penelitian ini juga duketahui bahwa semakin kecil nilai penetrasi maka nilai daya dukung tiang yang dihasilkan akan semakin besar, begitu juga sebaliknya semakin besar nilai penetrasi maka nilai daya dukung tiang yang dihasilkan akan semakin kecil.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".