Highway Embankment Construction Over Soft Soils in the Lower Mainland of British Columbia
Bibliographic record
Abstract
This paper describes the successful construction of new highway embankments over soft soils alongside existing high-traffic volume highways in the Greater Vancouver area of British Columbia. The two projects consisted of highway widening from two lanes to four, construction of over 12 km of new embankment ranging in thickness from 1.5 m to 7.0 m, construction of nine new bridges, relocation of 2 km of railway and 1.2 km of water main, placement of embankment fills over existing sewer, water and gas utilities, and agricultural drainage improvements. Design and construction of the embankments involved: staged preload construction with surcharging to reduce post construction settlements, installation of wick drains and use of lightweight fill materials to reduce the construction time period, and geogrid-reinforced concrete block walls to retain the preload fills. Instrumentation was monitored on a frequent basis to provide data that allowed project geotechnical engineers to provide approval to allow each stage of preload fill placement to proceed in each work area. This careful approach to embankment construction on the soft soils was very successful as there were no soil failures on the project resulting from the embankment construction. The paper describes the soil conditions in the area, and the geotechnical challenges to embankment construction posed by these conditions. The embankment design features, staged preload construction methodology, and examples of preload monitoring results are presented.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| 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 | 0.000 |
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".