Algorithm Optimisation and Landslide Risk Assessment for Critical Soil Thickness Calculation on Slopes Based on 3D Numerical Simulation (FLAC-3D) and Geographic Information System (GIS)
Bibliographic record
Abstract
This study focuses on the Dingjiafen slope in Chuxiong City, China, with the aim of improving the accuracy of slope landslide risk prediction. Formulas for calculating the critical soil layer thickness at the onset of slope instability are derived based on the physical model of the slope. Using the Digital Elevation Model (DEM) and ArcGIS, the critical and maximum soil layer thickness of each slope unit are calculated to predict potential landslide areas. FLAC-3D is employed to simulate and analyze the slope’s stability under natural conditions, and the numerical simulation results are compared with the predictions in ArcGIS. The findings reveal variations in the critical and maximum soil layer thickness among different slope units due to diverse topography. The slope units on both sides of the Chumeng Highway slopes, with a critical soil layer thickness ( ) between 1 and 3 meters, are connected, aligning with the results of FLAC-3D three-dimensional numerical simulation and the actual sliding positions on-site. Applying this method to simulate the soil layer thickness at the critical state for each slope unit enables slope stability prediction, offering a new perspective for the analysis and prediction of slope stability.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".