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

研究成果: 雜誌貢獻期刊論文同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.

原文???core.languages.en_GB???
頁(從 - 到)1020-1025
頁數6
期刊Canadian Journal of Civil Engineering
48
發行號8
DOIs
出版狀態已出版 - 2021

指紋

深入研究「Automatic manpower allocation for public construction projects using a rough set enhanced neural network」主題。共同形成了獨特的指紋。

引用此