Model Penurunan Umur Layan terhadap Perubahan Nilai Parameter Desain Perkerasan Lentur dan Perkerasan Kaku Runway Bandar Udara
Bibliographic record
Abstract
ABSTRAKDalam desain jenis perkerasan di fasilitas Runway Bandar Udara dibedakan menjadi kaku dan lentur, memiliki karakteristik sistem distribusi tegangan sampai subgrade yang berbeda, terutama pada perubahan nilai parameter desain. Hal ini akan mempengaruhi penurunan umur layan. Penelitian ini bertujuan untuk memodelkan hubungan penurunan umur layan terhadap perubahan nilai parameter desain yaitu peningkatan annual departure lalu lintas pesawat udara, penurunan nilai CBR subgarde, serta penurunan kondisi modulus PCC pada perkerasan kaku dan setiap layer pada perkerasan lentur. Metode yang digunakan adalah Layered Elastic Theory bersumber pada FAA AC 150/5320-6G dengan software FAARFIELD V 2.0.18. Pada perkerasan lentur, penurunan CBR Subgrade memiliki nilai perubahan umur layan yang paling besar di antara penurunan modulus dan kenaikan annual departure yaitu y = 19,395 e-0,092x. Sedangkan pada perkerasan kaku, penurunan modulus memiliki nilai perubahan umur layan yang paling besar di antara penurunan CBR subgrade dan kenaikan annual departure yaitu y = 21,445 e-0,132x. Berdasarkan perbandingan model umur layan tersebut, maka perubahan paramater CBR subgrade paling berpengaruh pada perkerasan lentur, sedangkan perubahan parameter modulus PCC pada perkerasan kaku.Kata kunci: umur layan, CBR subgrade, annual departure, mutu material ABSTRACTIn the design of the type of pavement at the airport runway facility, it is distinguished into rigid and flexible, having different characteristics of the stress distribution system to subgrade, especially in changes in design parameter values. This will affect the decrease in service life. This study aims to model the relationship between reduced service life and changes in design parameter values, such as increase in annual departure of aircraft traffic, a decrease in the CBR subgarde value, and a decrease in PCC modulus conditions on rigid pavements and each layer on flexible pavements. The method used is Layered Elastic Theory based on FAA AC 150/5320-6G with FAARFIELD V 2.0.18 software. On flexible pavement, the decrease in CBR Subgrade has the largest change in service life between the decrease in modulus and the increase in annual departure, namely y = 19.395 e-0.092x. Whereas on rigid pavements, the decrease in modulus has the greatest change in service life between the reduction in CBR subgrade and an increase in annual departure, namely y = 21.445 e-0.132x. Based on the comparison of the service life models, the changes in CBR subgrade parameters have the most effect on flexible pavements, while changes in the PCC modulus parameters on rigid pavements.Keywords: Numerical Integration, Dredging Volume
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".