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Record W4407873206 · doi:10.1139/cgj-2024-0675

Experimental investigations on seepage erosion-interface shear behavior of suction caisson in sandy seabed

2025· article· en· W4407873206 on OpenAlexvenueno aff
Yaru Zhang, Lizhong Wang, Shengjie Rui, Mengtao Xu, Zhen Guo

Bibliographic record

VenueCanadian Geotechnical Journal · 2025
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsnot available
FundersNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsGeotechnical engineeringCaissonGeologySeabedSuctionErosionShear (geology)EngineeringGeomorphology

Abstract

fetched live from OpenAlex

To evaluate the seepage erosion during the installation of suction caissons, this study developed a seepage erosion-interface shear test system to simulate the erosion phenomena. First, a parametric analysis was conducted to systematically investigate the effects of main factors (consolidation pressure, particle size distribution, and inhomogeneous pressure) on sand erosion characterization, vertical deformation, seepage characterization, and the interface strength of suction caisson. Following this, interface shear tests were performed to compare and analyze the changes in caisson–sand interface strength before and after soil erosion. It is indicated that the soil erodibility initially increases and then decreases when increasing fine particles. Seepage channels form first at the bottom and become dominant near the caisson wall, enhancing the sand hydraulic conductivity. In addition, the soil inside the caisson is less stable under smaller consolidation pressure. After the erosion, changes in sand particle distributions significantly reduce the caisson–sand interface strength with the maximum reduction of approximately 50%. This study emphasizes the influence of seepage erosion on caisson in-service capacity, which needs more attention in practice.

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 score0.576
Threshold uncertainty score0.501

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 head0.010
GPT teacher head0.240
Teacher spread0.229 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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