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Record W4403254123 · doi:10.1016/j.jterra.2024.101021

Predicting terrain deformation patterns in off-road vehicle-soil interactions using TRR algorithm

2024· article· en· W4403254123 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Terramechanics · 2024
Typearticle
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsConcordia University
Fundersnot available
KeywordsTerrainDeformation (meteorology)AlgorithmComputer scienceGeotechnical engineeringGeologyGeographyCartography

Abstract

fetched live from OpenAlex

• Soil deformation patterns caused by off-road vehicle-soil interactions were systematically investigated. • Experiments were conducted in a controlled soil bucket environment to ensure consistent conditions. • The results showed distinct deformation patterns in the uppermost soil layer when using pneumatic wheels. • Track wheels created consistent deformation patterns in all soil layers. • Semi-empirical models were developed to describe these observed deformation behaviors. Soil deformation is one of the parameters affecting the performance of off-road vehicles, including traction, mobility, and steering. This study offers an examination of soil deformation resulting from interactions with pneumatic and track wheels. Experiments were conducted using a soil bin with a single-wheel test rig, equipped with both a standard agricultural tire and a customized track wheel. Three distinct levels of vertical loads (2, 3, and 4kN) and forward velocities (1, 2, and 3 km/h) were applied using the wheel tester. The displacement and deformation of the soil layers, visualized as a vertical cross-section along the motion path, were consistently prepared and photographed for all experiments. Image analysis was undertaken with MATLAB software to scale images and extract graphical data. The highest deformation, with a value of 60.86 mm, is associated with the interaction of a pneumatic wheel with a force of 4 kN, while the lowest deformation occurs when the soil interacts with a track wheel with a force of 2 kN, with a value of 25.05 mm. Furthermore, the fitted surfaces obtained using the optimization algorithm showed good convergence with the experimental data, with R 2 values of 0.9783 and 0.9516 for the pneumatic tire and tracked tire, respectively. The results demonstrated that the TRR model performs well in accurately predicting soil deformation induced by various types of wheels. A comparison between soil deformations caused by track wheels and pneumatic wheels revealed that track wheels result in less deformation and disturbance, particularly in the upper soil layers. These findings underscore the importance of considering the type of traction device and loading conditions when assessing soil deformation in agricultural environments.

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.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.966
Threshold uncertainty score0.725

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.254
Teacher spread0.241 · 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