MétaCan
Menu
Back to cohort
Record W4298140458 · doi:10.1002/nag.3449

Analytical solution for consolidation of sand‐filled nodular pile (SFNP) foundation and its engineering application

2022· article· en· W4298140458 on OpenAlexaff
Juntao Wu, Fan Sun, M. Hesham El Naggar, Shuang Zhao, Kuihua Wang

Bibliographic record

VenueInternational Journal for Numerical and Analytical Methods in Geomechanics · 2022
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsWestern University
FundersNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsConsolidation (business)PileGeotechnical engineeringDrainagePore water pressureFoundation (evidence)Bearing capacityEngineeringGeologyGeography

Abstract

fetched live from OpenAlex

Abstract The sand‐filled nodular pile (SFNP) foundation is newly proposed to improve the bearing capacity and accelerate consolidation settlement of foundations installed in soft clay soil. By filling sand while driving the nodular pile into the soft clay soil, the SFNP foundation provides an extra drainage path with the aid of the nodular segment, while maintaining the high modulus reinforcement body at the core of the composite foundation. The performance of the SFNP foundation is greatly influenced by its consolidation behaviour. This study develops a generalized model for the SFNP foundation, and a closed‐form solution is then established considering the smear and well resistance effects. The developed model and the associated analytical solution are employed to investigate the consolidation rate and load distribution of the SFNP foundation. Finally, the analytical solution is utilized to evaluate the performance of SFNP foundation for an expressway project located in Zhejiang, China. The predicted dissipation of the excess pore water pressure employing the developed analytical solution agreed well with measured response from the field test, which further confirmed the advantages of the SFNP foundation and the reliability of the developed solution.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.941
Threshold uncertainty score0.579

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.022
GPT teacher head0.332
Teacher spread0.309 · 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
GenreMethods

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

Citations6
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal for Numerical and Analytical Methods in GeomechanicsSame topicGeotechnical Engineering and Soil StabilizationFrench-language works237,207