A Study on Causes and Stability of Masonry Retaining Walls by Case Analysis
Bibliographic record
Abstract
Masonry retaining walls, which have been used since long time, have recently been widely used in the construction of small complexesas an easy-to-purchase material and eco-friendly structure. Disasters owing to the collapse of these masonry retaining walls have frequently occurred over time. The purpose of this study is to quantitatively evaluate the causes for the collapse of masonry retainingwalls by collecting and analyzing collapse data using collapse phenomenon field trips, literature review, and search tools, and to use this analysis data, to suggest measures to prevent the collapse of masonry retaining walls. As a result of collecting and analyzing collapse case data, this study found that the main causes of masonry retaining wall collapse were the loss of foundation ground (scour), ground displacement owing to the excavation of adjacent land, settlement owing to ground softening because of rainfallinfiltration, poor drainage, and inclination of stonework. The causes for collapse were of eight types, including slope sliding additionalloading, and vibration, and 16 types when literature data were included. An analysis of the collapse frequency based on the causes for collapse revealed that the two causes for collapse, foundation and drainage, accounted for approximately 60% of the total cause,and measures for stability and Governing law of critical factor were proposed using this result.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".