Bibliographic record
Abstract
This study presents a finite element analysis on the behavior of soil nail walls subjected to different freezing and thawing conditions. Thermo-mechanical coupled analysis has become increasingly important due to the increasing number of geotechnical structures built in cold regions. Soil nailing is a relatively new technique of reinforcing existing slopes or supporting new excavations. Its performance has been proven through decades of successful applications throughout the world. However, its applications in cold regions are still very limited due to sparse research available on its behavior in cold regions. In addition, soil may dramatically change its properties after experiencing freezing and thawing cycles. A two-dimensional finite element analysis using ABAQUS software is conducted to investigate the frost penetration depth in the soil and the resulting influence on the performance of the soil nail walls in Northern Ontario. The numerical model is verified against the field measurements obtained from a research program done in Brunswick, Maine, U.S. (Duchesne, 2003). The change of thermally induced stress along soil nails as well as the increase of displacements and earth pressure on the facing of the walls is investigated under freezing conditions. A parametric study is also conducted to probe the effect of thermal insulation systems on the wall facing and nails. This study provides valuable insight into the behavior of soil nailed structures in cold regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".