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Record W4212988877 · doi:10.1139/cgj-2021-0069

Field investigation into the performance of pipe pile in soft clay under static and cyclic axial loads

2022· article· en· W4212988877 on OpenAlexvenueno aff
Chun-Yin Peng, Renpeng Chen, Jianfu Wang, Hanlin Wang

Bibliographic record

VenueCanadian Geotechnical Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
FundersZhejiang UniversityHunan Provincial Science and Technology Department
KeywordsPileGeotechnical engineeringPore water pressureMaterials scienceDissipationCyclic stressDynamic load testingStress (linguistics)Composite materialStructural engineeringGeologyEngineering

Abstract

fetched live from OpenAlex

We performed a field investigation into the effective stress, the shaft friction at the pile–soil interface, and the pile-end base resistance of a 29.5 m long prestressed concrete pipe pile in soft clay under static and cyclic loadings (50 000 cycles). Our results indicate that under low-level cyclic loading (CLR ≤ 0.2, where CLR is the ratio of cyclic load amplitude to the ultimate capacity), the pore water pressure at the pile–soil interface initially accumulates with the number of cycles, followed by gradual dissipation after reaching its maximum value. The effective stress also initially decreases and then increases. The shaft friction increases after cyclic loading. Owing to axial load redistribution, the base resistance decreases with the number of cycles. When the pile is subjected to high-level cyclic loading (CLR ≥ 0.5), the pore water pressure at the pile–soil interface keeps accumulating while the effective stress continues to decrease, leading to the degradation of shaft friction. The base resistance increases as a result of axial load redistribution. In particular, we analyzed the effects of the static load level and the cyclic load level on the changes shaft friction.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.702

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 head0.006
GPT teacher head0.184
Teacher spread0.178 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

Explore more

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