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Record W2067083719 · doi:10.1680/geng.12.00133

Effectiveness of granular columns in containing settlement

2014· article· en· W2067083719 on OpenAlexfundno aff
V. Sivakumar, Brendan C. O’Kelly, Catherine Moorhead, M. R. Madhav, Pauline MacKinnon

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Geotechnical Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
FundersQueen's UniversityQueen's University Belfast
KeywordsGeotechnical engineeringSettlement (finance)Water tableGeologyGroundwaterBearing capacityColumn (typography)EngineeringStructural engineering

Abstract

fetched live from OpenAlex

Laboratory-based research studies and full-scale evaluations of the behaviour of ground improved with granular columns are ample regarding bearing capacity, but limited in respect to the settlement response. This paper presents a laboratory model study that considers the settlement performance of isolated pad footings bearing on reinforced sand deposits under the influence of a fluctuating groundwater table. This is a particularly onerous condition for loose sand deposits in coastal areas, which may undergo significant collapse settlement over time. Loose and dense experimental sand beds were constructed, and the performance of rigid footings under a maintained load and bearing on sand incorporating different column configurations was monitored under cycling of the water table over a period of 28 d, with one filling/empting cycle every 18 h. It was found that settlement, while greatly reduced compared with that for unreinforced footings, was ongoing, and typically occurred at a much greater rate for loose sand than for dense sand. Also, settlement rates were slightly higher for fully penetrating than partially penetrating columns, and also for footings reinforced by a column group rather than a single column. This was attributed to the migration of sand grains into the larger column voids.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.135
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.004
GPT teacher head0.182
Teacher spread0.177 · 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.

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

Citations8
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Civil Engineers - Geotechnical EngineeringSame topicGeotechnical Engineering and Soil StabilizationFrench-language works237,207