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Record W4220729018 · doi:10.1061/9780784484012.002

Hybrid Ground Improvement Solution in Deep Compressible Glacial Lake Clay

2022· article· en· W4220729018 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGeo-Congress 2022 · 2022
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsConsolidation (business)ScheduleCivil engineeringGeotechnical engineeringSubgradeHuman settlementEngineeringComputer scienceBusiness

Abstract

fetched live from OpenAlex

A one- to two-story-high behavioral health hospital, designed with shallow foundations for an allowable bearing pressure of up to 95.8 kPa, was proposed in Dearborn, Michigan. Due to the presence of a 23-m-thick deposit of very soft to soft Great Lakes clays, the 1.2-m raise-in-grade and the proposed building were expected to induce as much as 12.7 cm of long-term consolidation settlement, negatively impacting the structure performance. The design-build team proposed hybrid ground improvement methods consisting of Prefabricated Vertical Drains (PVDs) and high modulus Rigid Inclusions (RIs) to overcome design performance and construction time-constraint challenges. Integrated ground improvement systems, coupled with detailed construction monitoring, are more cost and schedule effective than conventional deep foundations for similar projects and geologies. Aided by a thorough instrumentation program, the ground improvement design team was able to make timely decisions to adjust surcharging duration, achieve the necessary settlements, and still meet the project delivery schedule.

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.

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 score0.039
Threshold uncertainty score0.883

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.196
Teacher spread0.190 · 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