ANALISIS KERUSAKAN JALAN PADA RUAS JALAN RAYA JEPARA – BANGSRI KABUPATEN JEPARA DENGAN MENGGUNAKAN METODE BINA MARGA DAN PCI ( PAVEMENT CONDITION INDEX )
Bibliographic record
Abstract
Jalan Raya Jepara - Bangsri merupakan Jalan Provinsi dan termasuk jalan yang mengalami kerusakan jalan yang cukup parah. Sehingga diperlukan adanya analisis yang mengkaji tentang beberapa jenis kerusakan jalan tersebut serta penanganan yang sesuai dengan kerusakan jalan tersebut. Berdasarkan hasil analisis yang didapat jenis kerusakan yang terjadi pada ruas Jalan Raya Jepara-Bangsri KM 16 s/d KM 18 jika ditinjau dengan metode PCI dan Bina Marga memiliki peresentasi kerusakan sebesar Retak buaya (38,75%), Retak Blok (17,39%), Retak Pinggir (8,89%), Lubang (17,49%), Tambalan (12,99%), Pengausan (10,89%), dan amblas (3,59). Untuk penilaian kondisi jalan dengan metode PCI diperoleh rata-rata sebesar 55,523 yang termasuk dalam kategori sedang (Fair), sedangkan untuk metode Bina Marga diperoleh rata-rata nilai urutan prioritas sebesar 7,2. Hasil dari keduanya memiliki jenis penanganan yang sama yaitu Pemeliharaan Rutin. Untuk menghindari kerusakan jalan yang semakin parah maka perlu adanya perhitungan mengenai rencana tebal lapis tambahan perkerasan lentur. Berdasarkan hasil jenis nomogram 3 dengan Ipt = 2,0 dan Ipo = 4 sehingga diperoleh tebal perkerasan laston AC-WC (lapis aus) tebal 7,5 cm, AC-BC (lapis antara) tebal 10 cm, dan Lapis pondasi Sirtu kelas A tebal 12 cm.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".