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Record W2036892606 · doi:10.1193/1.1586121

Evaluation and Reduction of Liquefaction Potential at a Site in St. Louis, Missouri

2000· article· en· W2036892606 on OpenAlexaboutno aff
Sanjeev Kumar

Bibliographic record

VenueEarthquake Spectra · 2000
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsLiquefactionGeologySoil liquefactionGeotechnical engineeringSoil waterDynamic compactionCompactionSoil science

Abstract

fetched live from OpenAlex

This paper presents results of seismic ground response and liquefaction analyses performed for a site located in the floodplain of the Missouri River in St. Louis, Missouri. Synthetic earthquake time histories and recorded ground motions from two earthquakes in Canada were used to perform the analyses. Synthetic time histories were generated using attenuation relationships for Central United States. The ground response analyses indicated that the ground motions at the site are likely to amplify by a factor between 1.4 and 2.25. From the liquefaction analysis it was concluded that the site had significant liquefaction potential. The site was remediated to reduce liquefaction potential using deep dynamic compaction. Modifications, in the form of construction of stone columns, were made to the conventional method of deep dynamic compaction to densify the soils to required depths. Results presented show that site remediation procedure used at the site successfully densified the site soils to desired densities. Construction of stone columns not only densified the in‐place soils to deeper depths, but also helped to support relatively heavily loaded columns on spread footings. The project was completed on schedule with significant cost savings.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score0.642

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 head0.005
GPT teacher head0.204
Teacher spread0.198 · 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

Citations2
Published2000
Admission routes1
Has abstractyes

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