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Record W4416196677 · doi:10.1051/e3sconf/202566202003

Numerical investigation on the interaction effect of back-to-back MSE walls with different facing conditions

2025· article· fr· W4416196677 on OpenAlexaff
Fuxiu Li, Richard J. Bathurst, Yewei Zheng

Bibliographic record

VenueE3S Web of Conferences · 2025
Typearticle
Languagefr
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsMechanically stabilized earthParametric statisticsThrustInteractionUltimate tensile strengthReinforcementSoil structure interactionRetaining wall

Abstract

fetched live from OpenAlex

The behavior of back-to-back mechanical stabilized earth (MSE) walls is significantly influenced by the geometry, especially the horizontal distance between the back of the reinforced soil zone of the two MSE walls. In the Federal Highway Administration (FHWA) design guidelines, two cases are considered based on the horizontal distance. However, no justification is provided for this assumption. Therefore, research is needed to investigate the interaction effect of back-to-back MSE walls with different horizontal distances. In addition, the assumption does not provide a clear provision regarding facing conditions. This paper presents a numerical investigation of the interaction effect of the static behavior of back-to-back MSE walls with different facing conditions. A parametric study is conducted to investigate the effect of horizontal distance, soil friction angles, and wall heights on the lateral soil thrust and required tensile strength. Results show that the maximum facing displacement, lateral soil thrust, and required reinforcement tensile force of the back-to-back MSE walls with different facing conditions generally increase nonlinearly with increasing horizontal distance up to a certain critical value. Design recommendations that account for the interaction effect between the back-to-back MSE walls with different facing conditions on external stability and internal stability are provided.

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 score0.284
Threshold uncertainty score0.606

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 head0.015
GPT teacher head0.238
Teacher spread0.223 · 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

Explore more

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