Prediction of Rut Depth in Soil Caused by Wheels Using Artificial Neural Networks

Document Type : Research Article

Authors

• N. Farhadi
• A. Mardani
• A. Hosainpour
• B. Golanbari

Department of Mechanics Engineering of Biosystems, Urmia University, Urmia, Iran

https://doi.org/10.22067/jam.2024.90273.1295

Abstract

Introduction
The formation of ruts induced by vehicle traffic poses a significant challenge for agricultural soils due to soil compaction both at the surface and deeper layers. This phenomenon compromises vehicle performance increases energy consumption, and leads to long-term environmental degradation, such as soil erosion and fertility reduction. To enhance vehicle performance and reduce soil damage, it is crucial to accurately predict how factors such as vehicle speed, vertical load, and the number of passes impact rut depth. The findings of this study hold significant practical implications, facilitating the development for the creation of more efficient agricultural practices, while simultaneously minimizing environmental impact. The complexity of these interactions necessitates using machine learning models, especially artificial neural networks (ANNs), to predict rut depth based on input parameters. In this study, two machine learning models, namely the multilayer perceptron (MLP) and the radial basis function (RBF) networks, were employed to predict rut depth.
Materials and Methods
Experiments were conducted using a soil bin that allows for precise control of independent parameters, measuring 24 meters in length, 2 meters in width, and 0.8 meters in depth. The soil used was agricultural soil, comprising 35% sand, 22% silt, and 43% clay, with a moisture content of 8%. The tests included three independent parameters: vertical load (2, 3, and 4 kN), forward speed (1, 2, and 3 km h^-1), and number of wheel passes (up to 15). Two types of traction devices, including a rubber wheel and a track wheel, were tested. A caliper was used to measure the rut depth after each pass with an accuracy of 0.02 mm. The data collected from soil bin tests were used to train neural network models in MATLAB 2021-b software. The MLP model had a topology with two hidden layers and included three inputs and one output. In the RBF model, the network topology had a single hidden layer. The trial-and-error method was used to adjust the hyperparameters of the neural networks, including the number of neurons in the hidden layers, the learning rate, and momentum for the MLP network, as well as the spread rate and regularization rate for the RBF network.
Results and Discussion
Experimental data confirmed that increasing the vertical load and the number of passes resulted in deeper ruts. Conversely, an increase in speed led to a reduction in rut depth, particularly during the initial pass. Both artificial neural network (ANN) models accurately predicted rut depth, with the multilayer perceptron (MLP) neural network outperforming the radial basis function (RBF) neural network. Specifically, the root mean square error (RMSE) for the optimal MLP model, which utilized a learning rate of 0.001 and a momentum of 0.67, was 0.10. In contrast, the optimal RBF model, with an expansion rate of 0.23456, yielded an RMSE of 0.12. The findings indicate that the MLP artificial neural network model surpasses the RBF neural network model in terms of accuracy and overall performance. However, the RBF neural network exhibits a faster response time, making it particularly suitable for real-time applications.
Conclusion
This study demonstrates the efficacy of machine learning techniques, particularly artificial neural networks (ANNs), in predicting rut depth caused by off-road vehicle traffic. Both multilayer perceptron (MLP) and radial basis function (RBF) neural networks exhibited robust predictive capabilities, with the MLP model providing slightly superior accuracy and the RBF model offering better computational efficiency. These findings highlight the potential of machine learning in modeling complex interactions between soil and vehicles, which can enhance vehicle performance, mitigate soil erosion, and guide the design of off-road vehicles. Future research directions could include investigating additional soil parameters, various vehicle configurations, and the real-world implementation of autonomous off-road vehicles to promote more environmentally sustainable operations.

Keywords

• Multiple passes
• Soil bin
• Terramechanics
• Vertical load
• Vehicle speed

Main Subjects

• The relationship between machine and soil

©2025 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

References

1. Calleja-Huerta, A., Lamandé, M., Green, O., & Munkholm, L. J. (2023). Impacts of load and repeated wheeling from a lightweight autonomous field robot on the physical properties of a loamy sand soil. Soil and Tillage Research, 233, 105791. https://doi.org/10.1016/j.still.2023.105791
2. Cambi, M., Certini, G., Neri, F., & Marchi, E. (2015). The impact of heavy traffic on forest soils: A review. Forest Ecology and Management, 338, 124-138. https://doi.org/10.1016/j.foreco.2014.11.022
3. Champati, B. B., Padhiari, B. M., Ray, A., Jena, S., Sahoo, A., Mohanty, S., Patnaik, J., Naik, P. K., Panda, P. C., & Nayak, S. (2023). Implementation of multilayer perceptron (MLP) and radial basis function (RBF) neural networks for predicting Shatavarin IV content in Asparagus racemosus accessions. Industrial Crops and Products, 191, 115968. https://doi.org/10.1016/j.indcrop.2022.115968
4. Golanbari, B., & Mardani, A. (2024). An analytical model for stress estimation at the soil-tire interface using the dynamic contact length. Journal of Terramechanics, 111, 1-7. https://doi.org/10.1016/j.jterra.2023.08.006
5. Golanbari, B., Mardani, A., Farhadi, N., & Reina, G. (2024). Machine learning applications in off-road vehicles interaction with terrain: An overview. Journal of Terramechanics, 116, 101003. https://doi.org/10.1016/j.jterra.2024.101003
6. Golanbari, B., Mardani, A., Hosainpour, A., & Taghavifar, H. (2024). Modeling Soil Deformation for Off-Road Vehicles Using Deep Learning Optimized by Grey Wolf Algorithm. Journal of Agricultural Machinery, 14(1), 69-82. https://doi.org/10.22067/jam.2023.84339.1188
7. Golanbari, B., Mardani, A., Hosainpour, A., & Taghavifar, H. (2025). Predicting terrain deformation patterns in off-road vehicle-soil interactions using TRR algorithm. Journal of Terramechanics, 117, 101021. https://doi.org/10.1016/j.jterra.2024.101021
8. Kashaninejad, M., Dehghani, A. A., & Kashiri, M. (2009). Modeling of wheat soaking using two artificial neural networks (MLP and RBF). Journal of Food Engineering, 91(4), 602-607. https://doi.org/10.1016/j.jfoodeng.2008.10.012
9. Liu, K., Ayers, P., Howard, H., & Anderson, A. (2010). Influence of soil and vehicle parameters on soil rut formation. Journal of Terramechanics, 47(3), 143-150. https://doi.org/10.1016/j.jterra.2009.09.001
10. Machuga, O., Shchupak, A., Styranivskiy, O., Krilek, J., Helexa, M., Kováč, J., Kuvik, T., Mancel, V., & Findura, P. (2023). Field and Laboratory Research of the Rut Development Process on Forest Roads. Forests, 15(1), 74. https://doi.org/10.3390/f15010074
11. Mardani, A. (2014). On-the-move monitoring of tire rut depth on deformable soil using an instrumented inclinometer. Transactions of the ASABE, 57(5), 1291-1295. https://doi.org/10.13031/trans.57.10563
12. Mardani, A., & Golanbari, B. (2024). Indoor measurement and analysis on soil-traction device interaction using a soil bin. Scientific Reports, 14(1), 10077. https://doi.org/10.1038/s41598-024-59800-2
13. Pentoś, K., & Pieczarka, K. (2017). Applying an artificial neural network approach to the analysis of tractive properties in changing soil conditions. Soil and Tillage Research, 165, 113-120. https://doi.org/10.1016/j.still.2016.08.005
14. Sadeghi, S., Solgi, A., & Tsioras, P. A. (2022). Effects of traffic intensity and travel speed on forest soil disturbance at different soil moisture conditions. International Journal of Forest Engineering, 33(2), 146-154. https://doi.org/10.1080/14942119.2022.2055442
15. Tabatabaekoloor, R. (2016). Field evaluation of soil sinkage under different moisture content, traffic and loading rate. International Conference on Agricultural Engineering, CIGR- AgEng 2016, Aarhus, Denmark, 26- 29 June.
16. Taghavifar, H., & Mardani, A. (2014). Effect of velocity, wheel load and multipass on soil compaction. Journal of the Saudi Society of Agricultural Sciences, 13(1), 57-66. https://doi.org/10.1016/j.jssas.2013.01.004
17. Toivio, J., Helmisaari, H. S., Palviainen, M., Lindeman, H., Ala-Ilomäki, J., Sirén, M., & Uusitalo, J. (2017). Impacts of timber forwarding on physical properties of forest soils in southern Finland. Forest Ecology and Management, 405, 22-30. https://doi.org/10.1016/j.foreco.2017.09.022
18. Vennik, K., Keller, T., Kukk, P., Krebstein, K., & Reintam, E. (2017). Soil rut depth prediction based on soil strength measurements on typical Estonian soils. Biosystems Engineering, 163, 78-86. https://doi.org/10.1016/j.biosystemseng.2017.08.016
19. Vennik, K., Kukk, P., Krebstein, K., Reintam, E., & Keller, T. (2019). Measurements and simulations of rut depth due to single and multiple passes of a military vehicle on different soil types. Soil and Tillage Research, 186, 120-127. https://doi.org/10.1016/j.still.2018.10.011