Settlement Due to Blasting Improvement in Loose Saturated Deposits; Application to 18 Case Studies
Bibliographic record
Abstract
Among various methods for modification and treatment of saturated loose deposits, Explosive Compaction (EC) can be realized as an effective and common deep soil improvement method. In this paper, 18 different sites from U.S, Canada, India, Nigeria, Poland and etc. have been studied. Explosive Compaction successfully has been performed for modifying of loose saturated soils layers with thicknesses varied from 5 to 40 m in these sites. While EC performance, volume change due to densification ranged about 2% to 10% was observed. Analysis on the compiled database focused for Powder Factor (PF) role in induced settlement due to EC. Moreover, by using nonlinear optimization, a new relation has been proposed for settlement prediction in which PF, depth of Explosive charges and number of explosion phase’s factor have been considered simultaneously. This relation indicates it is required to increase used powder factor by increasing depth of charge to get an enough compaction as well as influence of first and second phases in final settlement is more than further phases
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".