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Record W4410891795 · doi:10.1680/jgeot.24.01042

Radial drainage and consolidation of soft soil subjected to intermittent cyclic loading

2025· article· en· W4410891795 on OpenAlexaff
Buddhima Indraratna, Shashika Atapattu, Cholachat Rujikiatkamjorn, Serge Leroueil, Richard Kelly

Bibliographic record

VenueGéotechnique · 2025
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsConsolidation (business)DrainageGeotechnical engineeringGeologyEnvironmental scienceMaterials scienceBusiness

Abstract

fetched live from OpenAlex

The rail network invariably encounters soft subgrades consisting of shallow estuarine clayey deposits. Cyclic loading generated by the passage of trains causes deformation and corresponding development of excess pore water pressure (EPWP), which dissipates during the rest periods between two consecutive trains. This paper presents an experimental study describing the effect of yield stress and EPWP responses upon intermittent cyclic loading (i.e. with rest periods), and the associated consolidation with the combination of vertical and radial drainage by way of a prefabricated vertical drain (PVD). Based on the laboratory data, the normalised yield stress for cyclic loading (NYCL) is introduced as an insightful parameter to define a novel empirical relationship between the yield stress, cyclic stress amplitude and the initial effective stress. The experimental results indicate that, as the NYCL increases, the peak EPWP decreases and, during the rest periods, the EPWP reaches a stable equilibrium faster without causing further settlement. Furthermore, this study demonstrates that the accumulated EPWP caused by cyclic loading can be further reduced when using a larger width of PVD for a given unit cell radius. An analytical model inspired by empirical parameters for predicting EPWP is proposed, capturing the effects of NYCL and the PVD characteristics.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.587
Threshold uncertainty score0.573

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 head0.004
GPT teacher head0.205
Teacher spread0.201 · 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 designBench or experimental
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

Citations2
Published2025
Admission routes1
Has abstractyes

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