Predicting terrain deformation patterns in off-road vehicle-soil interactions using TRR algorithm
Bibliographic record
Abstract
• 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".