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Record W4225114499 · doi:10.11159/icgre22.218

Numerical Analysis of Unpaved Roads Subjected to Surface Maintenance

2022· article· en· W4225114499 on OpenAlexvenueno aff
Bárbara Gonçalves Mourão, Ennio M. Palmeira, Juan Félix

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsComputer scienceSurface (topology)Geotechnical engineeringGeologyMathematicsGeometry

Abstract

fetched live from OpenAlex

Geosynthetics have proven to be beneficial in reinforcing soils, especially in problems with large deformations, such as unpaved roads built on soft soils and subjected to high loads. Due to heavy machinery traffic, this situation occurs even during the construction period of the road, in which is necessary to perform surface maintenance to the execution of the fill layers. Several experimental studies have indicated an improvement in the mechanical performance of unpaved roads when subjected to surface maintenance. The present research aimed at numerically investigate the behavior of unreinforced and reinforced unpaved roads subjected to surface maintenance. The analyses consisted of the following steps: determination of geostatic stresses in the subgrade; inclusion of the fill layer, as well as the geosynthetic reinforcement at the interface between the materials; application of a distributed load on the fill; unloading; execution of the surface maintenance from the deformed configuration of the fill; and reapplication of the load. The results indicate that the mechanical behavior of these roads will be better represented by the sequence described. Furthermore, relevant information on loads in the reinforcement and influence of material properties were obtained.

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 score0.423
Threshold uncertainty score0.882

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 head0.003
GPT teacher head0.176
Teacher spread0.173 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Civil, Structural, and Environmental EngineeringSame topicInfrastructure Maintenance and MonitoringFrench-language works237,207