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Record W4385070349 · doi:10.1680/jgein.23.00026

Thermo-hydraulic numerical modelling of in-soil conditions in reinforced soil walls

2023· article· en· W4385070349 on OpenAlexaff
A. Moncada, Ivan Puig Damians, S. Olivella, Richard J. Bathurst

Bibliographic record

VenueGeosynthetics International · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsRoyal Military College of Canada
FundersUniversitat Politècnica de CatalunyaMinisterio de Economía y Competitividad
KeywordsRelative humidityDegree of saturationEnvironmental scienceGeotechnical engineeringHumiditySoil waterSoil scienceGeologyHydrology (agriculture)MeteorologyGeography

Abstract

fetched live from OpenAlex

The role of temperature and relative humidity in long-term mechanical and chemical degradation of polyester fibres due to hydrolysis and creep is well documented. This study presents the results of a thermo-hydraulic 2D finite-element model used to estimate the magnitude and distribution of in situ temperature, relative humidity, and degree of saturation in the backfill of reinforced soil walls (RSWs) due to changes in atmospheric boundary conditions. Boundary conditions for in-air temperature, relative humidity and daily precipitation were taken from weather databases for continental, Mediterranean, desert, and tropical climates. Scenarios with different water tables, and permeable or impermeable zones around the reinforced soil zone were analyzed. Numerical outcomes show that mean in-soil temperature values can be related to the mean annual atmospheric value for each geographical location, with relevant fluctuations limited to the first 3 m of distance from the vertical and horizontal boundaries. In-soil relative humidity values depended on the climate dataset and the permeability of the zones adjacent to the reinforced soil. The results of this study and lessons learned are a valuable precursor for future studies of coupled thermo-hydro-mechanical modelling of polyester geosynthetic RSWs under in situ operational conditions.

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 score0.096
Threshold uncertainty score0.459

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 head0.017
GPT teacher head0.227
Teacher spread0.210 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueGeosynthetics InternationalSame topicGeotechnical Engineering and Soil StabilizationFrench-language works237,207