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Record W2035785388 · doi:10.1061/41098(368)9

Analysis of Seawall Concepts Using Yielding Soil Anchors

2010· article· en· W2035785388 on OpenAlexaff
Robert Harn, Ralph Petereit, Bill Perkins, John Arnesen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsTransport Canada
Fundersnot available
KeywordsSeawallPileGeotechnical engineeringEngineeringCivil engineeringMasonryGeology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score0.296

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.242
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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