- Bekker, M. G. (1957). Latest developments in off-the-road locomotion. Journal of the Franklin Institute, 263(5), 411-423. https://doi.org/10.1016/0016-0032(57)90281-8
- Fernandes, M. M. H., Coelho, A. P., da Silva, M. F., Bertonha, R. S., de Queiroz, R. F., Furlani, C. E. A., & Fernandes, C. (2020). Estimation of soil penetration resistance with standardized moisture using modeling by artificial neural networks. CATENA, 189, 104505. https://doi.org/10.1016/j.catena.2020.104505
- Gheshlaghi, F., & Mardani, A. (2021). Prediction of soil vertical stress under off-road tire using smoothed-particle hydrodynamics. Journal of Terramechanics, 95, 7-14. https://doi.org/10.1016/j.jterra.2021.02.004
- Haykin, S. (1999). Neural networks: a comprehensive foundation prentice-hall upper saddle river. NJ MATH Google Scholar.
- He, J., Wu, D., Ma, J., Wang, H., & Li, Y. (2019). Study on the Influence Law of Loading Rate on Soil Pressure Bearing Characteristics. Engineering Letters, 27(4).
- Kruger, R., Els, P. S., & Hamersma, H. A. (2023). Experimental investigation of factors affecting the characterisation of soil strength properties using a Bevameter in-situ plate sinkage and shear test apparatus. Journal of Terramechanics, 109, 45-62. https://doi.org/10.1016/j.jterra.2023.06.002
- Mahboub Yangeje, H., & mardani Korani, A. (2021). Design and Fabrication of a Bevameter for Measuring the Soil Deformation Details. Iranian Journal of Biosystems Engineering, 52(3), 487-498. https://doi.org/10.22059/ijbse.2021.318526.665385
- Pham, B. T., Nguyen, M. D., Bui, K. T. T., Prakash, I., Chapi, K., & Bui, D. T. (2019). A novel artificial intelligence approach based on Multi-layer Perceptron Neural Network and Biogeography-based Optimization for predicting coefficient of consolidation of soil. CATENA, 173, 302-311. https://doi.org/10.1016/j.catena.2018.10.004
- Pieczarka, K., Pentoś, K., Lejman, K., & Owsiak, Z. (2018). The use of artificial intelligence methods for optimization of tractive properties on Silty Clay Loam. Journal of Research and Applications in Agricultural Engineering, 63(1).
- Roul, A. K., Raheman, H., Pansare, M. S., & Machavaram, R. (2009). Predicting the draught requirement of tillage implements in sandy clay loam soil using an artificial neural network. Biosystems Engineering, 104(4), 476-485. https://doi.org/10.1016/j.biosystemseng.2009.09.004
- Taghavifar, H., & Mardani, A. (2014a). 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
- Taghavifar, H., & Mardani, A. (2014b). Prognostication of vertical stress transmission in soil profile by adaptive neuro-fuzzy inference system based modeling approach. Measurement, 50, 152-159. https://doi.org/10.1016/j.measurement.2013.12.035
- Taghavifar, H., Mardani, A., & Hosseinloo, A. H. (2015). Appraisal of artificial neural network-genetic algorithm based model for prediction of the power provided by the agricultural tractors. Energy, 93, 1704-1710. https://doi.org/10.1016/j.energy.2015.10.066
- Taghavifar, H., Mardani, A., Karim-Maslak, H., & Kalbkhani, H. (2013). Artificial Neural Network estimation of wheel rolling resistance in clay loam soil. Applied Soft Computing, 13(8), 3544-3551.
- Van, N. N., Matsuo, T., Koumoto, T., & Inaba, S. (2008). Experimental device for measuring sandy soil sinkage parameters. Bulletin of the Faculty of Agriculture Saga University, 93(1), 91-99.
- Wong, J. Y. (2010). Chapter 2 - Modelling of Terrain Behaviour. In J. Y. Wong (Ed.), Terramechanics and Off-Road Vehicle Engineering (Second Edition) (Second Edi, pp. 21-63). Butterworth-Heinemann. https://doi.org/10.1016/B978-0-7506-8561-0.00002-6
- Zhang, Z. X., & Kushwaha, R. L. (1999). Applications of neural networks to simulate soil-tool interaction and soil behavior. Canadian Agricultural Engineering, 41(2), 119.
|