Design of Fuel Tank Foundations on Soft Clay Deposit
Bibliographic record
Abstract
A foundation design method and considerations for two large-sized fuel tanks on soft marine clay deposit in Attawapiskat, located in the James Bay coastal area of Northern Ontario, Canada are describes. The tanks with diameter of 29 m and height of 12 m are required for fuel supply for a diamond mine, located approximately 100 km west of Attawapiskat. Each tank has a volume capacity of 7.5 million litres; and the maximum tank pressure of 140 kPa is exerted. The design involving a mat foundation is introduced. The design criteria require adequate safety margin against potential failure (bearing pressure and edge pressure) and relatively stringent settlement (differential) limits. Other design considerations include frost effects, limited availability of granular and rockfill materials, short construction period, and spill containment. Stability assessment indicates the edge failure to be more critical than the bearing capacity failure mode, and emphasizes the need for soil improvement. The uses of geosynthetic reinforcement along the perimeter of the tank and prefabricated vertical drains (PVDs) in the foundation soil in conjunction with site preloading are expected to reinforce the foundation, accelerate consolidation and reduce post-construction settlement. The design studies confirm the feasibility of constructing a mat foundation within short time, provided that the ground improvement measures mentioned above were incorporated.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".