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Record W4408385481 · doi:10.1061/ijgnai.gmeng-10393

Development of a Finite-Difference–Discrete-Element Coupled Model for Ram-Compacted Bearing Base Piles

2025· article· en· W4408385481 on OpenAlexaff
Adhila Haris, Hany El Naggar, Mohamed A. Shahin, Navid Bahrani

Bibliographic record

VenueInternational Journal of Geomechanics · 2025
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsDalhousie University
Fundersnot available
KeywordsBearing (navigation)Base (topology)Finite element methodGeotechnical engineeringStructural engineeringGeologyEngineeringComputer scienceMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

Ram-compacted bearing base (RBB) piles have been utilized as an effective foundation technique in China over the last 20 years. RBB is an enlarged technique for base piling that has proven to be a more economical and environmentally friendly solution than traditional piling methods. However, this technique remains unexplored in Western countries, and there have been only limited studies on the modeling and design of RBB piles. This paper discusses the development of three-dimensional (3D) numerical modeling of RBB piles by coupling the finite-difference method (FDM) and the discrete-element method (DEM). The explicit FDM program FLAC3D (version 7.0) was used to simulate the behavior of soil surrounding the pile wherein the material is represented by a number of continuum zones. The 3D DEM software Particle Flow Code (PFC3D7.0) was also used to simulate the flow and interaction of rubble particles through deformable contacts and time-domain solution of the equation of motion. The strengthened layer where the rubble base is constructed was simulated using a bonded particle model. The performance of the developed FDM-DEM model was then validated by comparing its results with site test results obtained from three case histories, and the outcomes were found to be in good agreement.

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: none
Teacher disagreement score0.850
Threshold uncertainty score0.499

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 integrity0.0000.000
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 head0.016
GPT teacher head0.252
Teacher spread0.236 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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