State of the knowledge of vegetation impact on soil strength and trafficability

Wendy L. Wieder, Sally A. Shoop

Journal of Terramechanics, Volume 78, 2018, Pages 1-14, ISSN 0022-4898,
https://doi.org/10.1016/j.jterra.2018.03.006.

Abstract: Researchers in a variety of fields have studied using vegetation to alter or reinforce soils. However, using vegetation for soil preservation in long-term land management of military training areas used for off-road vehicle maneuvers is more recent. Much of the work reported in the literature deals with trees and larger shrubs, appropriate for slope and bank stabilization. Other research efforts are for agricultural or forestry applications and involve crops, and again, large trees. This review discusses the issue of vegetation and its effect on a variety of soil strength parameters. It also reviews work regarding the effect of vehicle operations on vegetation and conversely the effect of vegetation on vehicle performance, or trafficability. The reviewed test methods and proposed soil strength models, based on a variety of soil properties, provide a basis for continuing work on models to evaluate areas used for off road military vehicle operations.

Keywords: Trafficability; Soil strength; Terrain; Impact; Mobility; Biomass; Vegetation
 

FE-DEM with interchangeable modeling for off-road tire traction analysis

Kenta Nishiyama, Hiroshi Nakashima, Taiki Yoshida, Hiroshi Shimizu, Juro Miyasaka, Katsuaki Ohdoi

Journal of Terramechanics, Volume 78, August 2018, Pages 15-25, ISSN 0022-4898, https://doi.org/10.1016/j.jterra.2018.03.005.

Abstract: This study examines a new finite element/discrete element method (FE-DEM) with interchangeable modeling between FEM and DEM for tire traction analysis. In the method, named iFE-DEM, the soil in a soil bin is modeled initially by FEM except for the region under or near the tire, which is modeled using DEM. When the FEM tire model starts to travel over DEM soil elements, the updated tire location will activate new conversion of modeling from FEM to DEM so that the zone of influence around the contact interface between tire and soil can be analyzed continuously using DEM. Those mobilized DEM elements rearward of the tire might be converted again to FEM elements by assuming that the effect of the stress state in DEM generated by tire travel might be negligible. The computational time for two-dimensional iFE-DEM analysis of a slip of 40% using the smallest region of initial DEM under the tire could be reduced to 23% of that obtained using DEM only soil modeling.

Keywords: FEM; DEM; Interchangeable modeling; Soil-wheel system; Traction performance

Experimental study of a tracked mobile robot’s mobility performance

Weidong Wang, Zhiyuan Yan, Zhijiang Du

Journal of Terramechanics, Volume 77, 2018, Pages 75-84, ISSN 0022-4898
https://doi.org/10.1016/j.jterra.2018.03.004.

Abstract: This paper proposes an experimental method of predicting the traction performance of a small tracked mobile robot. Firstly, a track-terrain interaction model based on terramechanics is built. Then, an experimental platform of the tracked robot is established, on which the measurement methods of the parameters that influencing the accuracy of the prediction model are introduced and the data post-processing are improved, including drawbar pull, slip ratio, sinkage, track deformation and so on. Based on the experimental data, several key terrain parameters are identified. With the tracked robot platform, the drawbar pull-slip ratio relationship is tested, and the effects on drawbar pull considering different kinds of terrain and the influence of the grousers are analyzed as well. The research results provide a reference for the experimental study on the traction performance of small tracked robots.

Keywords: Tracked mobile robot; Terrain–track interaction; Drawbar pull; Tractive performance; Experimental study

Determination of soil density by cone index data

György Pillinger, Attila Géczy, Zoltán Hudoba, Péter Kiss

Journal of Terramechanics, Volume 77, 2018, Pages 69-74, ISSN 0022-4898,
https://doi.org/10.1016/j.jterra.2018.03.003.

Abstract: In this paper a novel soil-density determination method is presented. The classic method (sampling, drying, mass measuring, etc.) can give proper results for the given problem but the standard methodology requires a lot of practical effort. While the soil is generally inhomogeneous, the measured density values of the soil sample applies only for the sample itself. On the entire soil territory this density can be interpreted only with significant errors. For a better mapping of the soil-density distribution expansive measurements are required. The task is complicated by the determination of density distribution in deeper layers of the soil as well. Our work presents a simpler method to determine the soil-density distribution in deeper layers with the use of cone penetration test (CI) results. With this method we can obtain detailed results of the soil-density distribution in deeper layers that may help further calculations for soil deformation analysis such as an exact determination of the soil sinkage below a tire track.

Keywords: Soil density; Deformation; Stubble; Cone penetration; Relative elongation
 

Improving accuracy of vehicle-terrain interface algorithms for wheeled vehicles on fine-grained soils through Bayesian calibration

Ian Dettwiller, Farshid Vahedifard, Masoud Rais-Rohani, George L. Mason, Jody D. Priddy

Journal of Terramechanics, Volume 77, 2018, Pages 59-68, ISSN 0022-4898,
https://doi.org/10.1016/j.jterra.2018.03.001.

Abstract: The Vehicle-Terrain Interface (VTI) model for off-road vehicle performance is often used in virtual prototyping to support preliminary vehicle design. Five VTI algorithms for wheeled vehicles on fine-grained soils are calibrated in this study using a Bayesian calibration technique and the Database Records for Off-road Vehicle Environments (DROVE): powered and unpowered sinkage, drawbar pull, motion resistance, and gross traction. These algorithms are improved through a two-stage Bayesian calibration technique utilizing the Metropolis algorithm with three separate calibration strategies. The VTI and proposed algorithms are compared for performance in coefficient of determination and root-mean square error. The results from each algorithm were validated through k-fold cross validation with five folds. The final algorithms from the best performing strategy after accounting for increased complexity for each of the five performance parameters are reported as calibrated algorithms. Validated improvements in coefficient of determination are recorded for all five parameters: 7.8% for powered sinkage, 1.4% for unpowered sinkage, 6.4% for drawbar pull, 0.9% for motion resistance, and 12.5% for gross traction. Improvements are also seen in the normalized root-mean square error performance: 13.4% for powered sinkage, 1.9% for unpowered sinkage, 7.6% for drawbar pull, 17.5% for motion resistance, and 23.2% for gross traction.

Keywords: Off-road mobility; Vehicle Terrain Interface (VTI) model; Bayesian calibration; Metropolis algorithm; Fine-grained soils; Sinkage; Drawbar pull; Traction; Motion resistance; Database Records for Off-road Vehicle Environments (DROVE)

Prediction effect of farmyard manure, multiple passes and moisture content on clay soil compaction using adaptive neuro-fuzzy inference system

Kamel Ghadernejad, Gholamhossein Shahgholi, Aref Mardani, Hafez Ghafouri Chiyaneh

Journal of Terramechanics, Volume 77, 2018, Pages 49-57, ISSN 0022-4898,
https://doi.org/10.1016/j.jterra.2018.03.002.

Abstract: Soil compaction by machine traffic is a complex process with many interacting factors. The strength of adaptive neuro-fuzzy inference system (ANFIS) is the ability to handle linguistic concepts and find nonlinear relationships between inputs and outputs parameters. In this research, the effect of farmyard manure, the number of tire passes, soil moisture contents and three average depths on clay soil compaction is predicted using ANFIS and Regression. For the prediction of soil compaction, an agricultural tractor tire was used and the experiments were carried out in the controlled condition of soil bin facility utilizing a well-equipped single-wheel tester. To measure soil compaction, cylindrical cores in groups of three were inserted into the three different depths. Various member function ANFIS were tested to discover the supervised ANFIS-based models for the soil compaction. On the basis of statistical performance criteria of MAPE and R2, Gaussian curve built-in membership function (gaussmf) was found as a proper model. In addition, the ANFIS model with ‘Gaussian mf’ is recommended considering the higher prediction performance values of MAPE = 0.2957%. The regression analyses of ANFI and Multiple Linear Regression (MLR) revealed a high correlation with farmyard manure, the number of tire passes, soil moisture, and depth. Also, it showed a higher performance compared to the regression model for predicting soil compaction. Thus, it can be concluded that ANFIS-based methodology is a soft computing approach that provides excellent nonlinear systems such as soil compaction.

Keywords: Farmyard manure; Multiple passes; Soil compaction; ANFIS; Multiple linear regression

Comparison of soil strength measurements of agricultural soils in Nebraska

Wendy Wieder, Sally Shoop, Lynette Barna, Trenton Franz, Catherine Finkenbiner

Journal of Terramechanics, Volume 77, 2018, Pages 31-48, ISSN 0022-4898, https://doi.org/10.1016/j.jterra.2018.02.003.

Abstract: In 2014 the University of Nebraska, Lincoln (UNL) was engaged in field testing program to investigate a soil moisture mapping system as a crop management tool. In conjunction with this work, the US Army Engineer Research and Development Center, Cold Regions Research and Engineering Laboratory (ERDC-CRREL) deployed a team to perform soil characterization and strength measurements at three agricultural test sites. The primary objective was an investigation of the Lightweight Deflectometer (LWD) as a soil surface strength tool for the purposes of assessing bearing capacity of soft soils. The LWD measurements were performed with those from more “standard” tests, i.e. the Dynamic Cone Penetrometer, Cone Penetrometer, and Clegg Impact Hammer to determine if the LWD produced results that compared with these methods. The strength test data were also used to calculate California Bearing Ratio (CBR) values using existing equations in order to see if the different test methods produced similar CBR values that could in turn be used to predict the bearing capacity of the sites. The secondary objective was to compare the strength data with the corresponding soil water content data taken by UNL to determine if soil moisture was an indicator of soil strength.

Keywords: Agricultural soils; Soil strength; Bearing capacity; DCP; CI; CIH; LWD; Soil water content

Effect of variations in front wheels driving lead on performance of a farm tractor with mechanical front-wheel-drive

Algirdas Janulevičius, Vidas Damanauskas, Gediminas Pupinis

Journal of Terramechanics, Volume 77, 2018, Pages 23-30, ISSN 0022-4898
https://doi.org/10.1016/j.jterra.2018.02.002.

Abstract: Most previous researches indicate that about 20–55% of available tractor power is lost in the process of interaction between tires and soil surface. Vertical wheel loads and tire performance are parameters that play a significant role in controlling slip and fuel consumption of a tractor. Tractor’s slip is adjusted by attaching additional weights and/or reducing tire pressures, and this may have an impact on driving lead of front wheels. Mechanical Front-Wheel-Drive (MFWD) tractors work efficiently when driving lead of front wheels is 3–4% in soft soil and 1–2% in hard soil. This research was aimed to experimentally determine such tire pressures that allow adjusting tractor’s slip without deviating from set value of driving lead of front wheels. The research was also aimed to determine the effect of driving lead of front wheels on MFWD tractor’s slip and fuel consumption. Experimental results showed that front/rear tire pressure combinations that generate a well-targeted driving lead of front wheels have no effect on slip on hard soil; however, it significantly affect fuel consumption. Results show that when air pressures in front/rear tires varied within 80–220 kPa, driving lead of front wheels varied in the range from +7.25% to −0.5%.

Keywords: Tractor performance; Driving lead of front wheels; Pressure combinations in tires; Slip; Fuel consumption

Detection of gullies in Fort Riley military installation using LiDAR derived high resolution DEM

Santosh Rijal, Guangxing Wang, Philip B. Woodford, Heidi R. Howard, J.M. Shawn Hutchinson, Stacy Hutchinson, Justin Schoof, Tonny J. Oyana, Ruopu Li, Logan O. Park,

Journal of Terramechanics, Volume 77, 2018, Pages 15-22, ISSN 0022-4898
https://doi.org/10.1016/j.jterra.2018.02.001.

Abstract: Intensive use of military vehicles in military installations create conditions favorable for gully formation. Gullies impede the access of vehicle, restrict the continuation of training, and lead to significant damage to vehicle and risk the life of soldiers. Therefore, it is critical to correctly identify the locations of gullies for continuous training mission. In this study, Fort Riley (FR) military installation was chosen as the study area. LiDAR derived 1 m resolution digital elevation model (DEM) acquired on 2010 was used to map the gullies. A procedure that measures local topographic position, i.e., difference from mean elevation (DFME) along with its integration to the land surface having high surface curvature values was employed. Two high spatial resolution WorldView-2 images of 2010 and field gully data collected in 2010 were utilized for accuracy assessment. Results showed that: (1) A total of 237 small and 166 large gullies were detected and most of them dominated the central west and northwest parts of the installation; (2) Based on the visual interpretation in the WorldView-2 images, there was no statistically significant difference between the detected and observed numbers of gullies; (3) Gullies measured in the field were well detected with an overall accuracy of 78%.

Keywords: DEM; Gully; Land degradation; LiDAR; Military training