Performance investigation of micropile groups in stabilizing unstable talus slopes via centrifuge model tests
Bibliographic record
Abstract
Micropile groups (MPGs) are an effective means of geological disaster prevention for small- and medium-sized landslides, with the advantages of light structures and convenient construction. However, the mechanical and deformation characteristics of MPGs are complex, and their practical application is ahead of theoretical research, which greatly limits the popularization and application of MPGs. This paper conducts a series of centrifuge model tests to investigate the mechanical and deformation characteristics and the antislip mechanism of MPGs, then compare them with those of conventional piles (CPs). In particular, MPGs with and without the platform in the strengthening process of the talus slope are compared, and monitoring the reinforcement effect of MPGs subjected to gravity loading. The results suggest that the soil pressure shows a triangular distribution pattern and is influenced by the position of the potential slip zone and the geometry of the bedrock surface. The compatibility deformation of the pile–soil leads to the stress release of the soil behind the pile, which is an important part affecting the antislip mechanism of the MPG. The platform limits 75% of the pile top displacement of the MPG and simultaneously redistributes the stress of the piles, providing a better overall antislip effect of the pile–soil composite.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".