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Record W4416196665 · doi:10.1051/e3sconf/202566203002

Developments in reliability-based design for reinforced soil structures

2025· article· fr· W4416196665 on OpenAlexaff
Richard J. Bathurst

Bibliographic record

VenueE3S Web of Conferences · 2025
Typearticle
Languagefr
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsProbabilistic logicLimit state designLimit (mathematics)Limit analysisStability (learning theory)Margin (machine learning)Mechanically stabilized earthFactor of safetyState (computer science)

Abstract

fetched live from OpenAlex

Much progress has been made to migrate from deterministic design approaches for mechanically stabilized earth (MSE) wall structures to (probabilistic) reliability-based design. The latter ensures that the margin of safety against not satisfying a particular limit state is expressed probabilistically. Deterministic factor-of-safety design, load and resistance factor design (LRFD) and partial factor approaches cannot provide the designer with the probability that a limit state is satisfied at time of design. A probabilistic approach provides a more nuanced appreciation of margins of safety in this regard. This extended abstract demonstrates the general approach using as examples the limit states associated with internal stability of geosynthetic MSE walls. An important feature of the general approach is a quantitative link to the deterministic factors of safety used in past practice. The same general approach can be applied to other cases such as reinforced fills over a void, and reinforced granular bases supporting a footing over soft foundations provided model bias statistics are available.

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 score0.922
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.241
Teacher spread0.225 · 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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