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Record W2954345906 · doi:10.1680/jgrim.17.00072

Load-sharing mechanism of non-displacement piled-raft foundation in sand

2019· article· en· W2954345906 on OpenAlexaff
Adel Hanna, Rouzbeh Vakili

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Ground Improvement · 2019
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsConcordia University
Fundersnot available
KeywordsRaftPileFoundation (evidence)Geotechnical engineeringSettlement (finance)Load sharingMechanism (biology)Displacement (psychology)EngineeringGeologyComputer scienceGeographyMaterials science

Abstract

fetched live from OpenAlex

Piled-raft foundations are recognised for sharing the total load by both the piles and the raft. The economic benefits of this system lead to its popularity in practice; however, research in this field has continued to lag behind owing to the difficulties associated with modelling and availability of field data. This is due to the fact that the load-sharing mechanism of the piled-raft foundation is a complex soil–structure interaction problem, which is governed by the pile/raft/geometry/soil condition and by their by-product parameters. This paper presents the results of an experimental investigation on a prototype model of a piled-raft foundation in sand. The effects of the governing parameters on the load-sharing mechanism are examined. The results of this investigation show that pile spacing and settlement govern the load-sharing mechanism between the piles and the raft. A widely accepted analytical model in the literature was modified in this study to take into account the effect of settlements and pile spacing on the load-sharing mechanism in a piled-raft system. The theory developed is validated by the current experimental results and those available in the literature. The design procedure is presented as a guide in the design of the piled-raft foundation in sand.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.276
Threshold uncertainty score0.836

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)0.0000.000

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 head0.005
GPT teacher head0.190
Teacher spread0.185 · 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 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

Citations11
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Civil Engineers - Ground ImprovementSame topicGeotechnical Engineering and Soil MechanicsFrench-language works237,207