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Record W2522127353 · doi:10.1080/19386362.2016.1236224

Load sharing ratio of pile-raft system in loose sand: an experimental investigation

2016· article· en· W2522127353 on OpenAlexaff
Mahmoud N. Hussien, Ebraheem H. Ramadan, Mohamed Hamed Hussein, Abdel-Aziz A. A. Senoon, Mourad Karray

Bibliographic record

VenueInternational Journal of Geotechnical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsPileRaftSettlement (finance)Geotechnical engineeringFoundation (evidence)Load sharingGeologyMaterials scienceComputer scienceGeography

Abstract

fetched live from OpenAlex

This technical note presents the results of a series of tests carried out on instrumented model pile-raft foundation to investigate the load sharing ratios between the foundation’s components where a loose sand layer is encountered near the ground surface. Four different pile-raft models with two different pile numbers and lengths were considered. In each model, the settlement of the raft and the load of each pile were monitored during the load application process. The experiments showed the effectiveness of pile length on reducing the overall settlement of the pile-raft system. The results showed also that the proportion of load transferred to any pile in a closely spaced pile group increases with its distance from the centre of the group. Unlike some previous studies that classified soil profiles containing loose sands near the surface as unfavourable situations for piled rafts, the experimental results herein represent an evidence of the efficiency of piled rafts even in soil profiles with loose sands immediately beneath the raft. Indeed, ignoring the contribution of the raft would lead to unnecessary over-conservatism in the design of piled rafts.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.222
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations16
Published2016
Admission routes1
Has abstractyes

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