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Record W4415937683 · doi:10.1139/cgj-2025-0030

Prediction of jacking force in circular press-in caisson in clay

2025· article· en· W4415937683 on OpenAlexvenueno aff
Xiaoxiang Wang, Maosong Huang, Zhongjie Zhang, Haoran Wang, Yaoliang Li

Bibliographic record

VenueCanadian Geotechnical Journal · 2025
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsJackingCaissonLateral earth pressureParametric statisticsFinite element methodExcavationReactionSafety factor

Abstract

fetched live from OpenAlex

The press-in caisson method, renowned for its precise construction control, has gained extensive application in both terrestrial and marine engineering projects in recent years. Accurate control of the downward jacking force within an optimal range is essential for ensuring construction safety and efficiency. This study presents an upper-bound solution for the jacking force of circular caisson on the basis of streamline-based velocity fields under axisymmetric conditions. The proposed soil failure mechanism is validated through comprehensive finite element analysis. Extensive parametric studies have been conducted to investigate the influence of key factors on the jacking force factor ( N j ), including the cutting-edge angle, caisson roughness, caisson radius, penetration depth, excavation extent, and soil strength. On the basis of the analytical and numerical results, predictive equations for upper-bound solutions of the circular caisson jacking force are developed, with particular emphasis on their reliability across cutting-edge angles ranging from 25° to 50°. The proposed methodology is further validated through a field case study of an actual caisson engineering project. This research provides valuable insights into the fundamental mechanisms governing caisson jacking force and offers a practical analytical tool for the design and construction of caisson foundations.

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 score0.158
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.009
GPT teacher head0.196
Teacher spread0.187 · 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

Citations10
Published2025
Admission routes1
Has abstractyes

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