Artificial Road Load Generation Using Artificial Neural Networks

Adebola Ogunoiki*, Oluremi Olatunbosun

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


This research proposes the use of Artificial Neural Networks (ANN) to predict the road input for road load data generation for variants of a vehicle as vehicle parameters are modified. This is important to the design engineers while the vehicle variant is still in the initial stages of development, hence no prototypes are available and accurate proving ground data acquisition is not possible. ANNs are, with adequate training, capable of representing the complex relationships between inputs and outputs. This research explores the implementation of the ANN to predict road input for vehicle variants using a quarter vehicle test rig. The training and testing data for this research are collected from a validated quarter vehicle model.

Original languageEnglish
Title of host publicationSAE Technical Papers
Place of PublicationUSA
PublisherSAE International
Publication statusPublished - 14 Apr 2015
EventSAE 2015 World Congress and Exhibition - Detroit, United States
Duration: 21 Apr 201523 Apr 2015


ConferenceSAE 2015 World Congress and Exhibition
Country/TerritoryUnited States


  • Tire
  • Modal Analysis
  • Finite Element Analysis

ASJC Scopus subject areas

  • Automotive Engineering
  • Safety, Risk, Reliability and Quality
  • Pollution
  • Industrial and Manufacturing Engineering


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