Static and cyclic responses of end-bearing and floating geosynthetics-encased steel slag columns via model tests
Bibliographic record
Abstract
Geosynthetic-encased stone column (GESC) has been widely adopted as a reinforcing technology for various civil projects in soft soils due to its enhancement of load-bearing capacity and drainage efficiency. Despite existing substantial body of research, the performance of GESC under cyclic loading and the potential benefits of using steel slag as an alternative aggregate remain underexplored. This study presents a comprehensive experimental investigation comparing the performance of end-bearing and floating geosynthetic-encased steel slag column (GESSC) under static and cyclic loading through model tests. Key parameters including settlement behavior, stress transfer efficiency, pore water pressure distribution, moisture migration, and undrained shear strength were systematically analyzed. The results demonstrate that end-bearing GESSC significantly outperforms floating column in terms of settlement control, load transfer to the bearing stratum, and pore pressure dissipation, especially under repeated cyclic loading. In contrast, floating columns more effectively mobilize shaft friction and enhance shear strength in the upper soil layers. These findings contribute to a better understanding of load transfer mechanisms in GESSC and provide practical guidance for their application in infrastructure projects subjected to dynamic loading conditions, while also promoting the reuse of steel slag as an environmentally beneficial fill material.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".