Estimation of Optimal Cone Index by Using Neural Networks

Rolling resistance is considered as a significant criterion in vehicles energy consumption. In off-road vehicles, soil and tire interaction encompasses more parameters that lead to the thorny problem in its analysis. The typical experimental models are useful remedies in the estimation perspective,...

Full description

Saved in:
Bibliographic Details
Published in:Arabian journal for science and engineering (2011) 2021-08, Vol.46 (8), p.7435-7443
Main Authors: Derafshpour, Sajjad, Valizadeh, Morteza, Mardani, Aref
Format: Article
Language:English
Subjects:
Energy consumption
Engineering
Humanities and Social Sciences
Interaction parameters
Mathematical models
Model accuracy
multidisciplinary
Multilayers
Neural networks
Off road vehicles
Radial basis function
Research Article-Mechanical Engineering
Rolling resistance
Science
Soil conditions
Soils
Vector analysis
Vertical loads
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Rolling resistance is considered as a significant criterion in vehicles energy consumption. In off-road vehicles, soil and tire interaction encompasses more parameters that lead to the thorny problem in its analysis. The typical experimental models are useful remedies in the estimation perspective, but they can not be extended to all conditions. Neural networks as efficient tools can simplify the examination procedure. This research was aimed to raise the Wismer–Luth model accuracy to rely on cone index as soil condition representative by utilizing three neural networks. Three types of networks were investigated, comprising multi-layer perceptron (MLP), radial basis function (RBF) and generalized regression neural network (GRNN). Various feature vectors consisting of vertical load parameters, velocity, traffic, and cone index values were evaluated to estimate the optimal value of the cone index. GRNN had the best performance with the least error rate in comparison with RBF and MLP. Based on the results of feature vector analysis, employing either forward velocity or multi-pass factors had a negligible effect on model improvement, but by incorporating all parameters (velocity, load, and number of passes), the accuracy of the model increased significantly.
ISSN:2193-567X
1319-8025
2191-4281
DOI:10.1007/s13369-020-05220-z