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Record W4416182657 · doi:10.1139/cgj-2025-0487

Analytical undrained bearing capacity models for spatially variable soils: mechanisms, uncertainty, and diagnostic maps

2025· article· en· W4416182657 on OpenAlexvenueno aff
Shangchuan Yang, Ben Leshchinsky, Yongxin Wu, Kai Cui, Zi-Jun Cao

Bibliographic record

VenueCanadian Geotechnical Journal · 2025
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsnot available
FundersKey Research and Development Program of Sichuan ProvinceOutstanding Youth Foundation of Jiangsu Province of ChinaNatural Science Foundation of Sichuan ProvinceInnovative Research Group Project of the National Natural Science Foundation of ChinaGovernment of Jiangsu ProvinceNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of ChinaSouthwest Jiaotong University
KeywordsBearing capacityRandom fieldRandom variableProbabilistic logicMonte Carlo methodSpatial variabilityField (mathematics)Bearing (navigation)Variable (mathematics)

Abstract

fetched live from OpenAlex

Soil strength exhibits natural spatial variability, which obscures the stochastic link between failure mechanisms and bearing capacity. This study establishes an analytical framework that couples multiple kinematically admissible mechanisms with random field theory to quantify the undrained bearing capacity of footings on soil with spatially variable strength. The analytical solutions show good agreement with numerical simulations in evaluating the undrained bearing capacity. Subsequently, the influence of spatial variability in undrained shear strength on the statistics of bearing capacity was investigated, providing insight into the distribution of critical failure mechanisms. Furthermore, this study introduces a failure-frequency map representing the spatial distribution of failure possibility, which can serve as a diagnostic tool to guide ground improvement from a random field perspective. Overall, the analytical model offers direct insights into critical failure mechanisms and their associated influence on bearing capacity under random field conditions, while still retaining the fidelity of probabilistic analyses involving extensive Monte Carlo simulations.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.986
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.011
GPT teacher head0.199
Teacher spread0.188 · 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.

Study designSimulation or modeling
Domainnot available
GenreMethods

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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