Lateral compression response of overlapping jet-grout columns with geometric imperfections in radius and position
Bibliographic record
Abstract
Spatial variability in the radius of a jet-grout column is commonly encountered in practice. Although various prediction models for the column radius are available, they have been generally used to predict a nominal radius. The radius variation within a column has been seldom considered. In this study, the intracolumn radius variation was simulated as a lognormal stochastic process. This was done based on the existing prediction models where the column radius can be correlated with the undrained shear strength of in situ soils. A slab consisting of overlapping jet-grout columns was considered. The slab serves as an earth-retaining stabilizing structure in a deep excavation. The effects of radius variation on the mass performance of the slab were examined with the finite-element method. In addition, the positioning errors in jet-grout columns were also investigated. Owing to the random nature of the radius variation, Monte-Carlo simulations were performed to estimate the statistical characteristics of the mass performance of the slab. A strength reduction factor was introduced and tabulated to account for the effects of geometric imperfections in the column radius and column position. With the strength reduction factor, practitioners could quantitatively evaluate the effects of these geometric imperfections in design considerations. Practical recommendations on the column length and column spacing were also proposed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".