Hierarchical response surface method for reliability analysis of a pile-slope system
Bibliographic record
Abstract
Stabilizing piles have been widely used as an effective measure to reinforce slopes. In this paper, a hierarchical response surface method is presented to evaluate the reliability of a pile-slope system efficiently. The suggested method can be used to identify the minimum reliability indexes of different types of failure modes. It can also be used to identify the representative failure modes governing the failure probability of the pile-slope system. This study found that the most critical sliding surface of an unreinforced slope and a reinforced slope is different. It may be nonconservative to design a pile-slope system according to the representative sliding surface of the slope without reinforcements. Even if many failure modes may exist, the reliability index of the pile-slope system is often controlled by several representative failure modes. For the slope examined in this paper, the reliability index of the pile-slope system is controlled by the reliability index of first representative failure mode. The first representative failure mode may vary with the reinforcement ratio, pile length, pile spacing, and location of the piles. The approach presented in this study provides a practical means to quantify the effect of such factors on the design of a pile-slope system.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".