Application of specialised in situ tests in Changi East reclamation projects, Singapore
Bibliographic record
Abstract
A land reclamation and ground improvement project requires an extensive study of underlying soils, fill material, performance of ground improvement works and shore-protection structures. The area of the Changi East project in Singapore is underlain by soft compressible soils, which will be filled with a greater thickness of granular soil; the project will create shore-protection structures. Therefore, the large magnitude of settlement and stability of shore-protection structures were major issues for the project. A ground improvement and engineering design process was required. This process required a detailed and comprehensive study of the ground profile and characterisation of underlying soils and fill material. Characterisation and interpretation of geotechnical parameters of soils applying specialised in situ testing has become popular due to its unique feature of measuring parameters under in situ conditions. The measured data from specialised in situ tests can be interpreted to obtain geotechnical parameters quickly in addition to soil classification and profiling without the need to collect samples. This paper presents application of specialised in situ tests as well as interpretation of measured data for land reclamation and ground improvement projects. This paper also discusses how these in situ testing methods were utilised to monitor and verify the progress of ground improvement.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".