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Record W3137509870 · doi:10.1139/cgj-2020-0623

Study on horizontal bearing characteristics of pile foundations in coral sand

2021· article· en· W3137509870 on OpenAlexvenueno aff
Chunyan Wang, Hanlong Liu, Xuanming Ding, Chenglong Wang, Qiang Ou

Bibliographic record

VenueCanadian Geotechnical Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
FundersNatural Science Foundation of ChongqingNational Natural Science Foundation of China
KeywordsGeotechnical engineeringPileBreakageGeologyCoralBending momentBearing capacityDisplacement (psychology)Foundation (evidence)Materials scienceEngineeringStructural engineeringComposite material

Abstract

fetched live from OpenAlex

This paper presents the horizontal bearing characteristics of piles in coral sand and silica sand from comparative experimental studies. Six model piles with different diameters were tested. The horizontal bearing capacity, deformation characteristic, bending moment, p–y curve, change in soil horizontal pressure, as well as particle breakage behaviour of coral sand were investigated. The results show that, in the coral sand foundation, the horizontal bearing capacities of piles and the increments in soil horizontal pressures are obviously greater than those in silica sand. Accordingly, the lateral displacement, rotation of the pile head, bending moment, and corresponding distribution depth in coral sand are significantly smaller than those in silica sand. The p–y curves indicate that the horizontal stiffness of coral sand is greater than that of silica sand. Remarkably, the breakage behaviour of coral sand is mainly distributed in the range of 10 times the pile diameter depth and 5 times the pile diameter width on the side where the sand is squeezed by the pile. Furthermore, in coral sand, the influence of pile size is more pronounced, the squeezing force generated by the pile spreads further, and its influence range is larger compared to those in silica 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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.546
Threshold uncertainty score0.654

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 integrity0.0000.001
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 head0.223
Teacher spread0.207 · 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

Citations36
Published2021
Admission routes1
Has abstractyes

Explore more

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