EVALUASI KEKUATAN STRUKTUR BAJA BERGELOMBANG BERDASARKAN TIPE DAN KETEBALAN
Bibliographic record
Abstract
Material konstruksi struktur baja bergelombang mempunyai tiga tipe yaitu standar, deep dan superdeep. Tiga tipe ini dibedakan dari dimensi penampang sehingga terdapat perbedaan nilai momen inersia, semakin besar momen inersia maka bentang maksimum yang diaplikasikan bisa semakin lebar. Di Indonesia material konstruksi ini sudah difungsikan sebagai struktur jembatan. Penilaian kekuatan konstruksi struktur baja bergelombang berdasarkan tiga kriteria yaitu compression failure, plastic hinge, dan connection failure. Pada saat perencanaan diperlukan iterasi perhitungan untuk mendapatkan pemilihan tipe dan ketebalan yang optimum, maka dilakukan perhitungan dan analisis pada lebar bentang 15 meter dengan bentuk konstruksi setengah lingkaran agar diketahui peningkatan persentase kekuatan setiap perubahan tipe dan ketebalan baja yang digunakan. Metode perhitungan berdasarkan pedoman yang dikeluarkan oleh Canadian Highway Bridge Designs Code. Berdasarkan hasil perhitungan semakin besar nilai momen Inersia akan semakin menambah kekuatan dari struktur baja bergelombang. Hasil evaluasi menunjukkan rata-rata penambahan kekuatan 3 parameter tinjauan pada masing-masing tipe dan ketebalan baja adalah untuk tipe standar kenaikan rata-rata nilai wall strength compression sebesar 9%, kenaikan rata-rata seam strength 3% dan kenaikan rata-rata plastic hinge 11%. Untuk tipe deep kenaikan rata-rata nilai wall strength compression sebesar 24%, kenaikan rata-rata seam strength 25% dan kenaikan rata-rata plastic hinge 19%. Untuk Tipe Superdeep kenaikan rata-rata nilai wall strength compression sebesar 41%, kenaikan rata-rata seam strength 27% dan kenaikan rata-rata plastic hinge 34%. Hasil tersebut memperlihatkan kenaikan rata-rata kekuatan tertinggi ada pada tipe baja bergelombang tipe superdeep.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".