Modeling Soil Pressure-Sinkage Characteristic as Affected by Sinkage rate using Deep Learning Optimized by Grey Wolf Algorithm

ماشین های کشاورزی

Article 13, Volume 14, Issue 1 - Serial Number 31, March 2024, Pages 69-82   

Document Type: Research Article-en

DOI: 10.22067/jam.2023.84339.1188

Authors

B. Golanbari¹; A. Mardani^{* 1}; A. Hosainpour¹; H. Taghavifar²

¹Department of Mechanics Engineering of Biosystems, Urmia University, Urmia, Iran

²Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Concordia, Canada

Abstract

Due to the numerous variables that may influence the soil-machine interaction systems, predicting the mechanical response of soil interacting with off-road traction equipment is challenging. In this study, deep neural networks (DNNs) are chosen as a potential solution for explaining the varying soil sinkage rates because of their ability to model complex, multivariate, and dynamic systems. Plate sinkage tests were carried out using a Bevameter in a fixed-type soil bin with a 24 m length, 2 m width, and 1 m depth. Experimental tests were conducted at three sinkage rates for two plate sizes, with a soil water content of 10%. The provided empirical data on the soil pressure-sinkage relationship served as the basis for an algorithm capable of discerning the soil-machine interaction. From the iterative process, it was determined that a DNN, specifically a feed-forward back-propagation DNN with three hidden layers, is the optimal choice. The optimized DNN architecture is structured as 3-8-15-10-1, as determined by the Grey Wolf Optimization algorithm. While the Bekker equation had traditionally been employed as a widely accepted method for predicting soil pressure-sinkage behavior, it typically disregarded the influence of sinkage velocity of the soil. However, the findings revealed the significant impact of sinkage velocity on the parameters governing the soil deformation response. The trained DNN successfully incorporated the sinkage velocity into its structure and provided accurate results with an MSE value of 0.0871.

Keywords

Bevameter; Deep neural network; Off-road vehicle; Soil bin; Terramechanics

Supplementary Files

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