Optimal profile for concave slopes under static and seismic conditions
Bibliographic record
Abstract
This study presents a methodology to determine the stability and optimal profile for slopes with concave cross section under static and seismic conditions. Concave profiles are observed in some natural slopes suggesting that such geometry is a more stable configuration. In this study, the profile of a concave slope was idealized by a circular arc defined by a single variable, the mid-chord offset (MCO). The proposed concave profile formulation was incorporated into a limit equilibrium–based log spiral slope stability method. Stability charts are presented to show the stability number, MCO, and mode of failure for homogeneous slopes corresponding to the most stable configuration under static and pseudostatic conditions. It is shown that concave profiles can significantly improve the stability of slopes. Under seismic conditions, the impact of concavity is most pronounced. Good agreement was demonstrated upon comparison of the results from the proposed method against those attended from a rigorous upper bound limit analysis. The proposed methodology, along with recent advances in construction technology, can be employed to use concave profiles in trenches, open mine excavations, earth retaining systems, and naturally cemented and stabilized soil slopes. The results presented provide a useful tool for preliminary evaluation for adopting such concave profiles in practice.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".