Analysis of Seawall Concepts Using Yielding Soil Anchors
Bibliographic record
Abstract
Stretching 8,016 feet (2,443 meters) along downtown Seattle's waterfront, the 75-year-old Alaskan Way Seawall provides the interface between the city's downtown core and Elliott Bay. Damage to the seawall during the 2001 Nisqually earthquake led to investigations that confirmed the seawall is deteriorating and seismically vulnerable to potential liquefaction of the loose soils that underlie the structure. The City of Seattle is planning to replace the seawall. To further advance the proposed replacement program, two replacement concepts were developed. One used secant pile technology while the other used yielding soil anchors in combination with soil cement (jet grouting). A soil-structure interaction program was used successfully to model the complex dynamic interaction between the soil, the existing seawall and timber relieving platform, and the structures proposed to replace the seawall. The structural and geotechnical engineers collaborated on the development of an innovative hybrid system in which the soil improvement or secant pile wall resists service loads and liquefied soil pressures while the yielding soil anchors resist the inertial effects of a seismic event. This paper will review the development of the replacement alternatives analysis, the lessons learned, and the importance of successful structural-geotechnical collaboration in order to provide a solution to a complex soil-structure interaction problem.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".