Landslide Potential Analysis on Hilly Terrain Based on the CPT Data along the Malang-Kediri Road, Indonesia
Bibliographic record
Abstract
Landslides are known to cause significant infrastructure damage and subsequently generate substantial economic losses.One area that is particularly susceptible to such events is the roadway connecting Malang-Kediri and Batu City, Indonesia.This corridor carries a high volume of traffic; therefore, any landslide occurrence has severe implications, including the complete disruption of transportation access.This study investigates the slope factor of safety (FS) to evaluate landslide potential along the Malang-Kediri connecting road using Cone Penetration Test (CPT) data.The FS of the existing slopes was assessed using CPT results as the primary field data to estimate soil physical and mechanical properties.Numerical simulations were conducted using GeoStudio (GeoSlope) Version 8.0 to compute FS values for natural, dry, and saturated (rainy-season) conditions.All analyses were performed under at-rest conditions, without including ground improvement measures.The results indicate that 4 out of 9 modeled scenarios exhibit instability: Slope S-1 under scenarios 2 and 3, and Slopes S-2 and S-3 under scenario 3.In general, slopes became unstable in scenario 3, where the combined effects of traffic loading and elevated pore water pressures substantially reduced shear resistance.Under these conditions, slopes demonstrated failure potential when FS < 1.07.The calculated FS values at points S-1, S-2, and S-3 were 0.55, 0.59, and 0.74, respectively, indicating critical instability.Field observations reveal that soft, organic, clay-rich soils in the upper layers dominate the unstable slope sections.These materials possess low shear strength and are highly susceptible to deformation under excessive loads.Consequently, under scenario 3 loading conditions, the inherent weakness of these soil layers contributes significantly to slope failure.Further investigations are necessary to design appropriate soil improvement strategies and slope reinforcement measures to mitigate future landslide risks along this critical transportation corridor.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".