Three-dimensional stability evaluation of a preexisting landslide with multiple sliding directions by the strength-reduction technique
Bibliographic record
Abstract
Landslide 1 is located on the right bank of the downstream side of the dam for Hongjiadu Hydropower Station in Guizhou Province, China. In view of its close proximity to important workings, the stability of the slide is of great concern. Ground investigation showed that the slide is potentially unstable and has two sliding directions. Reinforcement mainly by piles was proposed as the stabilization measure, but two key questions were raised during the design of the piles. (1) How could the stability of the slope be evaluated, as there are two major sliding directions? (2) How could the shear strength parameters of the slip band be determined, as it contains a significant proportion of coarse particles? In this paper, we demonstrate how these issues can be addressed by the strength-reduction technique. First, the strength parameters of the slip band are back-analyzed by assuming a factor of safety of unity. Second, the major sliding directions are determined by carrying out finite difference analyses. Third, two proposed stabilization schemes are evaluated and compared to demonstrate the versatility of the technique. The present study has demonstrated that the strength-reduction technique is a powerful tool for analyzing such problems.Key words: landslide, reinforcement, piles, stability evaluation, strength-reduction technique.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".