Bibliographic record
Abstract
Gravity caissons were employed as part of the wharf front structures for a container port terminal in Singapore. This paper reports the movements of eight consecutive gravity caissons supported on sand compaction piles (SCPs) with highly variable lengths of penetration. It is established that the caisson movements increase with an increase in the length of the SCP, as longer SCPs are necessary when hard strata are at greater depth. The large caisson movements observed during caisson infilling and backfilling do not pose a concern because the wharf deck beams connecting adjacent caissons can be adjusted. However, the caisson movements under service loads would affect the operation of the overlying quay cranes on top of the caissons. The present field study reveals that preloading the caissons is effective in reducing the caisson movements under service loads because the observed caisson movements are insignificant during subsequent unloading–reloading of the caissons. Back-analysis using the finite element method (FEM) shows that the observed caisson movements at different construction stages can be reasonably replicated. The numerical results are also used to evaluate the caisson tilt angle, which could not be measured in the present field study. The caisson tilt is found to be independent of the length of SCPs underneath a caisson.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".