A General Regression Neural Network model for construction equipment maintenance costs

H. L. Yip, Hongqin Fan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

This paper presents a time series analysis based on General Regression Neural Networks (GRNN) models to address the prediction of construction equipment maintenance costs. The results show that GRNN can model the behaviour and predict the maintenance costs for different equipment categories and fleet with satisfactory accuracy. The paper also discusses the effects of incorporation of the parallel fuel consumption data as explanatory time series to modelling performance.
Original languageEnglish
Title of host publicationProceedings - 2012 7th International Conference on Computing and Convergence Technology (ICCIT, ICEI and ICACT), ICCCT 2012
Pages1353-1357
Number of pages5
Publication statusPublished - 1 Dec 2012
Event2012 7th International Conference on Computing and Convergence Technology (ICCIT, ICEI and ICACT), ICCCT 2012 - Seoul, Korea, Republic of
Duration: 3 Dec 20125 Dec 2012

Conference

Conference2012 7th International Conference on Computing and Convergence Technology (ICCIT, ICEI and ICACT), ICCCT 2012
CountryKorea, Republic of
CitySeoul
Period3/12/125/12/12

Keywords

  • Construction equipment
  • General regression neural networks
  • Maintenance management
  • Time series analysis

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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