Investigating the Effect of Tractor's Tire Parameters on Soil Compaction Using Statistical and Adaptive Neuro-Fuzzy Inference System (ANFIS) Methods

被引:4
|
作者
Shahgholi, Gholamhossein [1 ]
Moinfar, Abdolmajid [1 ]
Khoramifar, Ali [1 ]
Maciej, Sprawka [2 ]
Szymanek, Mariusz [2 ]
机构
[1] Univ Mohaghrgh Ardabili, Dept Biosyst Engn, Ardebil 5619911367, Iran
[2] Univ Life Sci Lublin, Dept Agr Forest & Transport Machinery, PL-20950 Lublin, Poland
来源
AGRICULTURE-BASEL | 2023年 / 13卷 / 02期
关键词
inflation pressure; bulk density; ANFIS; tire; SUBSOIL COMPACTION; RUBBER TRACK; PREDICTION; PRESSURE; VEHICLES; NETWORK; STRESS; MODEL;
D O I
10.3390/agriculture13020259
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
Many factors contribute to soil compaction. One of these factors is the pressure applied by tires and tillage tools. The aim of this study was to study soil compaction under two sizes of tractor tire, considering the effect of tire pressure and traffic on different depths of soil. Additionally, to predict soil density under the tire, an adaptive neuro-fuzzy inference system (ANFIS) was used. An ITM70 tractor equipped with a lister was used. Standard cylindrical cores were used and soil samples were taken at four depths of the soil inside the tire tracks. Tests were conducted based on a randomized complete-block design with three replications. We tested two types of narrow and normal tire using three inflation pressures, at traffic levels of 1, 3 and 5 passes and four depths of 10, 20, 30 and 40 cm. A grid partition structure and four types of membership function, namely triangular, trapezoid, Gaussian and General bell were used to model soil compaction. Analysis of variance showed that tire size was significant on soil density change, and also, the binary effect of tire size on depth and traffic were significant at 1%. The main effects of tire pressure, traffic and depth were significant on soil compaction at 1% level of significance for both tire types. The inputs of the ANFIS model included tire type, depth of soil, number of tire passes and tire inflation pressure. To evaluate the performance of the model, the relative error (epsilon) and the coefficient of explanation (R-2) were used, which were 1.05 and 0.9949, respectively. It was found that the narrow tire was more effective on soil compaction such that the narrow tire significantly increased soil density in the surface and subsurface layers.
引用
收藏
页数:15
相关论文
共 50 条
  • [21] MI-ANFIS: A Multiple Instance Adaptive Neuro-Fuzzy Inference System
    Khalifa, Amine B.
    Frigui, Hichem
    2015 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE 2015), 2015,
  • [22] The Effect of Adaptive Neuro-fuzzy Inference System (ANFIS) on Determining the Leadership Perceptions of Construction Employees
    Keles, Abdullah Emre
    Haznedar, Bulent
    Keles, Muemine Kaya
    Arslan, Mustafa Turan
    IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING, 2023, 47 (06) : 4145 - 4157
  • [23] The Effect of Adaptive Neuro-fuzzy Inference System (ANFIS) on Determining the Leadership Perceptions of Construction Employees
    Abdullah Emre Keles
    Bülent Haznedar
    Mümine Kaya Keles
    Mustafa Turan Arslan
    Iranian Journal of Science and Technology, Transactions of Civil Engineering, 2023, 47 : 4145 - 4157
  • [24] DETERMINATION OF SENSORLESS INPUT PARAMETERS OF SOLAR PANEL WITH ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) METHOD
    Syafaruddin
    Abubakar, Muhammad Iqbal
    Soma, Hizkia Glorius
    Said, Sri Mawar
    Latief, Satriani
    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL, 2018, 14 (06): : 2259 - 2271
  • [25] PREDICTION OF BIOMASS PELLET DENSITY USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM(ANFIS) METHOD
    Liu, Juan
    Yan, Zhuoyu
    Xu, Mingze
    Liu, Yudi
    Bai, Xuewei
    Xiu, Yonghai
    Wei, Desheng
    INMATEH-AGRICULTURAL ENGINEERING, 2023, 70 (02): : 181 - 190
  • [26] Modeling of dielectric behavior of polymers nanocomposites using adaptive neuro-fuzzy inference system (ANFIS)
    Mohamed, R. A.
    EUROPEAN PHYSICAL JOURNAL PLUS, 2022, 137 (03):
  • [27] Dynamic characterization of flexible vibrating structures using adaptive neuro-fuzzy inference system (ANFIS)
    Ismail, A. Y.
    Ismail, R.
    Darus, I. Z. Mat
    2006 4TH STUDENT CONFERENCE ON RESEARCH AND DEVELOPMENT, 2006, : 155 - +
  • [28] Modeling of dielectric behavior of polymers nanocomposites using adaptive neuro-fuzzy inference system (ANFIS)
    R. A. Mohamed
    The European Physical Journal Plus, 137
  • [29] Predicting bending rigidity of woven fabrics using adaptive neuro-fuzzy inference system (ANFIS)
    Behera, B. K.
    Guruprasad, R.
    JOURNAL OF THE TEXTILE INSTITUTE, 2012, 103 (11) : 1205 - 1212
  • [30] Identification of the most influencing parameters on the properties of corroded concrete beams using an Adaptive Neuro-Fuzzy Inference System (ANFIS)
    Shariati M.
    Mafipour M.S.
    Haido J.H.
    Yousif S.T.
    Toghroli A.
    Trung N.T.
    Shariati A.
    Computers and Concrete, 2020, 25 (01): : 155 - 170