Evaluasi Perkuatan Eksisting Bronjong Pada Kasus Kelongsoran Jalan Cisasawi, Kecamatan Parongpong, Kabupaten Bandung Barat
Bibliographic record
Abstract
Abstrak Jalan Cisasawi mengalami kelongsoran pada tahun 2020. Usaha penanganan kelongsoran telah dilakukan oleh warga, menggunakan Bronjong. Hanya saja, perencanaan perkuatan tersebut tidak memperhitungkan persyaratan yang berlaku. Agar tidak terjadi kejadian serupa, perlu dilakukan evaluasi terhadap perkuatan tersebut. Evaluasi dimulai dengan pengumpulkan data dengan cara pengukuran geometri lereng dan pengujian tanah. Analisis stabilitas dilakukan menggunakan software Geostudio. Evaluasi dilakukan di akhir untuk mengetahui apakah konstruksi perkuatan lereng eksisting tersebut cukup aman atau tidak. Dari hasil analisis didapatkan bahwa nilai SF dari lereng eksisting (sebelum adanya bronjong) sebesar 0,504. Kondisi ini sesuai dengan lapangan yaitu lereng mengalami kelongsoran. Hasil analisis stabilitas lereng setelah diperkuat dengan Bronjong adalah SF sebesar 1,014. Nilai SF ini tidak memenuhi yang disyaratkan SNI 8460-2017 faktor keamanan dalam kondisi gempa SF lebih besar dari 1,1 sehingga diperlukan alternatif perkuatan tambahan lereng. Alternatif perkuatan tambahan lereng dilakukan memperbesar dimensi bronjong. Dari hasil analisis perkuatan tambahan didapatkan SF sebesar 1,277. Kata kunci: Perkuatan lereng, longsor, bronjong, angka keamanan Abstract Cisasawi Road experienced a landslide in 2020. Landslide handling efforts have been carried out by residents, in the form of strengthening gabions. However, the retrofitting plan does not take into account requirements. In order to avoid similar incidents, it is necessary to evaluate the reinforcement. This evaluation begins with collecting data by measuring slope geometry and soil testing. Stability analysis was performed using GeoStudio software. Evaluation is carried out at the end of the analysis to determine whether the existing slope reinforcement construction is safe enough or not. From the results of the analysis, it was found that the SF value of the existing slope (before the gabions) was 0.504. This condition is in accordance with the field, namely the slope is sliding. The result of slope stability analysis after reinforced with Gabions is SF 1.014. This SF value does not meet the required SNI 8460-2017 safety factor in earthquake conditions SF > 1.1 so that additional slope reinforcement alternatives are needed. An alternative to additional slope reinforcement is to increase the gabion dimensions. From the results of the additional reinforcement analysis, it was found that SF 1,277. Keywords: Slope reinforcement, landslide, gabion, safety factor
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".