Automatic manpower allocation for public construction projects using a rough set enhanced neural network

Jieh Haur Chen, Li Ren Yang, Jui Pin Wang, Shang I. Lin, Jiun Yao Cheng, Meng Hsueh Lee, Chih Lin Chen

Research output: Contribution to journalArticlepeer-review

Abstract

Accurate estimates of manpower are still heavily dependent on well-experienced personnel. The objectives of this study are to prove the feasibility of using rough set theory to classify and weigh the impact attributes, and to develop a model to assess the total quantities of labor needed for a construction project using a rough set enhanced artificial neural network (ANN). Experts suggest 14 attributes that influence the estimation of on-site manpower for construction projects. After trimming and analyzing the basic data, the rough set approach is used to classify and weigh the attributes into three levels of impact based on their frequency. A rough set enhanced ANN is accordingly developed that yields an accuracy rate of 91.903%, higher than that of a regular ANN. A practical and effective prediction model benefits personnel having to estimate on-site manpower needs for construction projects.

Original languageEnglish
Pages (from-to)1020-1025
Number of pages6
JournalCanadian Journal of Civil Engineering
Volume48
Issue number8
DOIs
StatePublished - 2021

Keywords

  • Artificial neural network (ANN)
  • Construction project
  • Manpower
  • Prediction
  • Rough set

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