Integrating Grey System Analysis and Empirical Method for Slope Stability Assessment in Road Engineering
Bibliographic record
Abstract
This work tackles the distortions generated by subjective factors in slope stability forecasts by proposing a unique method for predicting the stability of soil slopes in road engineering.To objectively identify the parameters impacting slope stability, the method combines gray relational analysis with simulation analysis performed using the ABAQUS program.First, measured data are used to create a finite element model in ABAQUS, and the finite element strength reduction method is used to calculate safety factors for slopes under different scenarios.The elements affecting soil slope stability are then assigned weights based on an enhanced grey relational analysis and a judgment matrix.The similarity between the known and unknown slopes is then evaluated using the gray relational degrees, allowing for precise prediction-making.The usefulness of the suggested technique in objectively calculating the weights of affecting components is demonstrated by numerical simulation results, with considerable weights assigned to parameters like cohesiveness and internal friction angle.Case studies demonstrate the precision and efficacy of the approach by showing how the enhanced grey relational analysis matches real-world circumstances exactly.The suggested method improves computation accuracy by taking into account a variety of influencing elements and removing subjective interference, making it a useful tool for precise slope forecasts in engineering projects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".