The relationship between machine and soil
N. Farhadi; A. Mardani; A. Hosainpour; B. Golanbari
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, ...
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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 MethodsExperiments 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 DiscussionExperimental 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.ConclusionThis 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.
The relationship between machine and soil
M. Naderi-Boldaji; H. Azimi-Nejadian; M. Bahrami
Abstract
Machinery traffic is associated with the application of stress onto the soil surface and is the main reason for agricultural soil compaction. Currently, probes are used for studying the stress propagation in soil and measuring soil stress. However, because of the physical presence of a probe, the measured ...
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Machinery traffic is associated with the application of stress onto the soil surface and is the main reason for agricultural soil compaction. Currently, probes are used for studying the stress propagation in soil and measuring soil stress. However, because of the physical presence of a probe, the measured stress may differ from the actual stress, i.e. the stress induced in the soil under machinery traffic in the absence of a probe. Hence, we need to model the soil-stress probe interaction to study the difference in stress caused by the probe under varying loading geometries, loading time, depth, and soil properties to find correction factors for probe-measured stress. This study aims to simulate the soil-stress probe interaction under a moving rigid wheel using finite element method (FEM) to investigate the agreement between the simulated with-probe stress and the experimental measurements and to compare the resulting ratio of with/without probe stress with previous studies. The soil was modeled as an elastic-perfectly plastic material whose properties were calibrated with the simulation of cone penetration and wheel sinkage into the soil. The results showed an average 28% overestimation of FEM-simulated probe stress as compared to the experimental stress measured under the wheel loadings of 600 and 1,200 N. The average simulated ratio of with/without probe stress was found to be 1.22 for the two tests which is significantly smaller than that of plate sinkage loading (1.9). The simulation of wheel speed on soil stress showed a minor increase in stress. The stress over-estimation ratio (i.e. the ratio of with/without probe stress) noticeably increased with depth but increased slightly with speed for depths below 0.2 m.
The relationship between machine and soil
B. Golanbari; A. Mardani; A. Hosainpour; H. Taghavifar
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 ...
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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.
The relationship between machine and soil
H. Mahboub Yangeje; A. Mardani
Abstract
IntroductionSeedbed preparation, seeding, and transplanting are usually based on mechanical soil tillage. Tillage by cutting, mixing, overturning, and loosening the soil can modify the physical, mechanical, and biological properties of soil. These days, because of soil protection, the use of tillage ...
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IntroductionSeedbed preparation, seeding, and transplanting are usually based on mechanical soil tillage. Tillage by cutting, mixing, overturning, and loosening the soil can modify the physical, mechanical, and biological properties of soil. These days, because of soil protection, the use of tillage tools is less and less recommended, and some implements such as cultivators are preferred to primary tillage tools such as plows. Experimental study of soil-tool interaction and field measurements of the mechanics of tillage tools are usually time-consuming and costly. On the other hand, the variety of variables and uncontrolled conditions add other dimensions to the complexity of this method. Also, the experimental and analytical methods do not have a comprehensive view of stress distribution and soil deformation in the soil-tool interaction process.Materials and MethodsThe main purpose of this study is to validate the results of numerical simulations in two phases of experimental tests: in soil bin environment and in finite element computer simulations. Experimental tests were performed in the soil bin environment of the Department of Mechanical Engineering of Biosystems, Urmia University, which has a soil bin facility with dimensions of length and width of 24 and 2 m, respectively, and has clay loam soil. Before experimental tests, soil preparation was performed by using some special tillage implements (harrow, leveler, and roller) which were attached to the soil bin (Figure.1). For experimental tests, a mechanism set consisting of two cultivator blades with a width of 15cm, a length of 20cm, and at a spacing of 35cm from each other was prepared and constructed. The relevant mechanism is designed to have the ability to change the tillage depth. Data were collected at three different soil depth levels of 6, 10, and 14cm in the soil bin with three replications. Data recording was performed using a 10-channel data logger with load cell connectivity and data storage ability. Also, in this study, the Drucker-Prager model as a finite element simulation method was used to calculate the stress during the soil-tool relationship. ABAQUS 6.10.1 software was used to simulate the cultivator tine. To solve the problem, the soil parameters were defined as presented in Table 1, and then the interaction between the soil-tool model and the necessary constraints, including boundary conditions, were defined. In the next step, meshing was applied to the constructed model.Results and DiscussionIn the results section, first, the results related to the amount of traction force required for the tillage tine in the simulation were calculated and then compared with the soil bin experimental tests. The traction force of the finite element simulation results for three tillage depths of 6, 10, and 14 cm in three principal directions is shown in Figure 4. A comparison of simulation and experimental results showed that there is a good agreement between them. In comparison, the simulation error range of the three depths of 6, 10, and 14 cm has shown 7.3, 5.6, and 4.16% at a speed of 2.5 kmh-1, respectively, as the velocity studied in this research. In the next section, the results of stress distribution contours in the soil and finally the overlap of the blade effect were discussed. Figure 6 shows the status of stress contours at three depths. By increasing the depth of the tine at the three depth levels studied, the stress range is shifted from the soil surface to its depth. For this purpose, at the maximum depth studied in this study (14 cm), it shows that the stress propagation to the soil surface is less than at other depths. Also, with decreasing depth, for a depth of 6 cm, the maximum stress was on the top soil surface, in other words, more deformation was seen on the soil surface.ConclusionComparing the simulation results for predicting traction force with the results of experimental tests has led to relatively acceptable results and the maximum traction force prediction error at different depths has been about 7.3%.The distribution of stress in the soil was observed due to the tine depth. The highest intensity of stress propagation was observed at the soil surface; and the highest soil surface deformation at a depth of 6 cm. With increasing depth, both parameters of stress and soil surface deformation have decreased. According to the results of the studied blades, it is better to use these types of tillage tools only at lower depths. Also, in evaluating the overlap of the soil loosening zone in the side-by-side tines, it proves the superiority of the tine performance at lower depths.
F. Gheshlaghi; A. Mardani
Abstract
Introduction: Rolling resistance is one of the most substantial energy losses when the wheel moves on soft soil. Rolling resistance value optimization will help to improve energy efficiency. Accurate modeling of the interaction soil-tire is an important key to this optimization and has eliminated the ...
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Introduction: Rolling resistance is one of the most substantial energy losses when the wheel moves on soft soil. Rolling resistance value optimization will help to improve energy efficiency. Accurate modeling of the interaction soil-tire is an important key to this optimization and has eliminated the need for costly field tests and has reduced the time required to test.
Rolling resistance will change because of the tire and wheel motion parameters and characteristics of the ground surface. Some tire design parameters are more important such as the tire diameter, width, tire aspect ratio, lugs form, inflation pressure and mechanical properties of tire structure. On the other hand, the soil or ground surface characteristics include soil type; moisture content and bulk density have an important role in this phenomenon. In addition, the vertical load and the wheel motion parameters such as velocity and tire slip are the other factors which impact on tire rolling resistance. According to same studies about the rolling resistance of the wheel, the wheel is significantly affected by the dynamic load.
Tire inflation pressure impacted on rolling resistance of tires that were moving on hard surfaces. Studies showed that the rolling resistance of tires with low inflation pressure (less than 100 kPa) was too high.
According to Zoz and Griss researches, increasing the tire pressure increases rolling resistance on soft soil but reduces the rolling resistance of on-road tires and tire-hard surface interaction. Based on these reports, the effect of velocity on tire rolling resistance for tractors and vehicles with low velocity (less than 5 meters per second) is usually insignificant.
According to Self and Summers studies, rolling resistance of the wheel is dramatically affected by dynamic load on the wheel.
Artificial Neural Network is one of the best computational methods capable of complex regression estimation which is an advantage of this method compared with the analytical and statistical methods.
It is expected that the neural network can more accurately predict the rolling resistance. In this study, the neural network for experimental data was trained and the relationship among some parameters of velocity, dynamic load and tire pressure and rolling resistance were evaluated.
Materials and Methods: The soil bin and single wheel tester of Biosystem Engineering Mechanics Department of Urmia University was used in this study. This soil bin has 24 m length, 2 m width and 1 m depth including a
single-wheel tester and the carrier.
Tester consists of four horizontal arms and a vertical arm to vertical load. The S-shaped load cells were employed in horizontal arms with a load capacity of 200 kg and another 500 kg in the vertical arm was embedded. The tire used in this study was a general pneumatic tire (Good year 9.5L-14, 6 ply)
In this study, artificial neural networks were used for optimizing the rolling resistance by 35 neurons, 6 inputs and 1 output choices. Comparison of neural network models according to the mean square error and correlation coefficient was used. In addition, 60% of the data on training, 20% on test and finally 20% of the credits was allocated to the validation and Output parameter of the neural network model has determined the tire rolling resistance. Finally, this study predicts the effects of changing parameters of tire pressure, vertical load and velocity on rolling resistance using a trained neural network.
Results and Discussion: Based on obtained error of Levenberg- Marquardt algorithm, neural network with 35 neurons in the hidden layer with sigmoid tangent function and one neuron in the output layer with linear actuator function were selected. The regression coefficient of tested network is 0.92 which seems acceptable, considering the complexity of the studied process. Some of the input parameters to the network are speed, pressure and vertical load which their relationship with the rolling resistance is discussed.
The results indicated that in general trend of changes, the velocity is not affected by rolling resistance. Rolling resistance increases when tire pressure decreases. This is due to energy consumption for creating deflection on the body of the tire at the lower levels of tire inflation pressure. Another variable parameter is the vertical load on the wheel and its logical relation with rolling resistance using neural network. The results showed that increasing the vertical load increases the rolling resistance.
Conclusions: The major purpose of this study was the feasibility of using learning algorithms for interaction between wheel and soil. The parameters of the wheel when clashes with soil are not stochastic and in spite of their complexity follow a specific model, certainly. Artificial neural network trained with a correlation coefficient of 0.92 relatively had a good performance in education, testing and validation parts. To validate the network results, the impact of some factors on the extraction process such as velocity, load and inflation pressure was simulated. The main objective of this article is comparing the network performance with basic principles and other scientific reports.
In this regard, the predictions by trained neural network indicated that rolling resistance is independent of the velocity of the wheel. On the other hand, rolling resistance decreases by increasing tire inflation pressure which is a general trend similar to other studies and reports in the same mechanical condition of the soil tested. Rolling resistance changes are directly proportional to load vertical variations on the wheel in terms of quantity and quality, similar to experimental models such as Wismer and Luth.
H. Taghavifar; A. Mardani
Abstract
Introduction: Tire tractive parameters of the driving wheel are of the most substantial factors for the evaluation of the performance of agricultural tractors. Great tractive efficiency has called the attention of vehicle designers to attain economic efficiency owing to the minimization of fuel consumption. ...
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Introduction: Tire tractive parameters of the driving wheel are of the most substantial factors for the evaluation of the performance of agricultural tractors. Great tractive efficiency has called the attention of vehicle designers to attain economic efficiency owing to the minimization of fuel consumption. At terrain-tire interface, some soil physical-mechanical changes occur that lead to unwanted soil compaction. Of the influential parameters for the creation of soil compaction is the soil stresses formed owing to the wheeled vehicle trafficking. While the increase of tractive efficiency is desired, minimization of soil stresses should also be considered with the same importance to make a trade-off between the aforementioned parameters. There are numerous studies documented in the literature that deal with the measurement of soil stress/strain data due to the wheeled vehicle trafficking and also those works that address the correlation between the soil stress and soil compaction. It is recognized that in order to reduce soil compaction both at topsoil and subsoil levels, the soil stress at the soil-tire interface should be reduced. There are various parameters that affect the tractive efficiency and the soil stress creation such as wheel load, slip, tire inflation pressure, velocity, etc. On the other hand, the wheel is subjected to the torques and forces exerted to the vehicle and the vehicle dynamics are significantly affected by the soil-wheel interactions. Survey of the literature shows that numerous studies have focused on the evaluation of tractive efficiency both in field test and controlled conditions in laboratories with the intention of increasing tractive efficiency. The studies dedicated to the soil mechanical strength are more engaged with the approaches to minimize the soil stress propagation. The present study considers both factors and considers the most influential tire parameters such as wheel, velocity and slip to assess the relationship between traction and the soil vertical stress in a soil profile using a single-wheel tester and a soil bin facility.
Materials and methods: The soil bin in Department of Mechanical Engineering of Urmia University was used in this study. This soil bin is featured 24 m in length, 2 m in width and 1 m in depth including a single-wheel tester and the carriage. A chain system was used for the power transmission from the electromotor to the carriage. The carriage was able to move alongside the soil bin through four ball bearings which also hold the weight of the carriage. The utilized tire in the study was a 220/65R21 driving wheel. One load cell was situated vertically to measure the wheel load and four S-shaped load cells were horizontally situated between the single-wheel tester and the carriage to measure the traction force. An electric motor was used to empower the carriage while another electric motor was used to empower the wheel tester. The difference between the linear velocities of the carriage and the wheel-tester provided the desired levels of slip. A housing including four load cells situated at the distances of 12.5 cm was used to measure the soil vertical stress transmission in the soil profile. The system was buried at the desired depth in the path of wheel traversal. Under the aforesaid treatments, the experiments were undertaken with the purpose of simultaneous measurement of soil stress propagation and traction force and finally the correlation between these parameters.
Results and discussion: The results were analyzed using the statistical analysis at 1% significance level. The results showed that an increase in traction force leads to an increment of vertical soil stress. It was also recognized that the reduction in the velocity leads to the increase in soil stress which is due to the greater contact duration between the soil and the tire. Also, an increase in wheel load results in an increase of soil stress which has a linear correlation with the traction force. Furthermore, it was deduced that the increase in depth leads to a reduction of soil vertical stresses.
Conclusions: The present study is aimed at investigating the effect of net traction force on the imposed vertical stress under the 220/65R21 driving wheel. Hence, velocity at three levels (i.e. 0.8, 1, 1.2 m s-1), wheel load at three levels (i.e. 2, 3, and 4 kN) and slippage at three levels (i.e. 8, 12, and 15%) were considered to obtain traction force and soil vertical stress at three depths of 0.1, 0.15 and 0.2 m. Experiments were carried out in the complete randomized block design with three replicates on clay loam soil at 12% moisture content. The vertical stress was measured using a manufactured soil stress transducer where the net traction was measured using four horizontally installed load cells between the tester rig and the carriage. A correlation was developed between soil stress and traction force. The results revealed that vertical stress increases with respect to increase of wheel load and slippage, whereas vertical stress decreases by increase in depth and velocity. Additionally, it was found that wheel load and slippage bring about increased traction force while velocity has no significant effect on traction force at 1% significance level. Finally, it was deduced that an increase of traction force results in an increase of vertical stress transmission.
H. Mohammadzadeh; A. Mardani; A. Modarres Motlagh
Abstract
The tire-mechanics models have been developed for the study of wheel movement on the road or soil surface while these models are unlikely to describe the motion of wheels on uneven surfaces. Due to dynamical complexity of this phenomena and the importance of this subject for farm conditions and the wheel ...
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The tire-mechanics models have been developed for the study of wheel movement on the road or soil surface while these models are unlikely to describe the motion of wheels on uneven surfaces. Due to dynamical complexity of this phenomena and the importance of this subject for farm conditions and the wheel carrier devices, the present research aimed to investigate the effects of several parameters on the wheel passing the obstacle. The experiments were carried out using single wheel tester in soil bin condition. The results indicated a relatively linear relationship between the impact force applied on tire and forward speed of wheel and also the height of rectangular obstacle. The effect of inflation pressure was inversed in the range of complete formed tire’s body on impact force and in low levels of tire inflation pressure; tire’s body damps the maximum impact forces. The medium levels of pressure (about 150-200 kPa) resulted in less horizontal force that applied on the wheel for different levels of forward speed and obstacle’s height. Tractive force for passing obstacle was increased by raising forward speed and the obstacle’s height.
