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Record W4385070098 · doi:10.1139/cgj-2021-0310

Displacement-based finite element approach on analysing flexible combined pile–raft foundation in layered soil

2023· article· en· W4385070098 on OpenAlexvenueno aff
Aniruddha Bhaduri, Deepankar Choudhury

Bibliographic record

VenueCanadian Geotechnical Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
Fundersnot available
KeywordsRaftPileGeotechnical engineeringFinite element methodParametric statisticsStructural engineeringFoundation (evidence)Displacement (psychology)GeologySoil structure interactionEngineeringMaterials scienceMathematicsComposite materialGeography

Abstract

fetched live from OpenAlex

The present study proposes a new finite element methodology to analyse the behaviour of flexible combined pile–raft foundation (CPRF) situated in layered soil, in a displacement-based framework. The soil medium is idealised as an advanced elastic Pasternak medium and the piles and raft are modelled as bar and plate element, respectively. The two components of CPRF are analysed simultaneously and displacement compatibility is satisfied at the pile–raft junctions. A number of soil–structure interaction factors, which govern the behaviour of CPRF, are suitably subsumed in the analysis scheme. The proposed method is validated with available analytical and experimental studies. Further parametric studies, investigating the effects of soil layering and raft flexibility on the behaviour of CPRF, are explored. It is observed that the load sharing proportion between the components and the raft deformation pattern depend upon the thickness and position of the soft soil layer in a multilayered soil system. The thickness of the flexible raft plays a pivotal role in determining the behaviour of CPRF, founded in a multilayered soil profile. Thus, this research manifests notable advancement in understanding the behaviour of flexible CPRF in layered soil.

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)0.000
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: none
Teacher disagreement score0.812
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.221
Teacher spread0.205 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Geotechnical JournalSame topicGeotechnical Engineering and Soil StabilizationFrench-language works237,207