Automatic manpower allocation for public construction projects using a rough set enhanced neural network-Reference-Cited by-同舟云学术

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

Published:2021-08 Issue:8 Volume:48 Page:1020-1025
ISSN:0315-1468
Container-title:Canadian Journal of Civil Engineering
language:en
Short-container-title:Can. J. Civ. Eng.

Author:

Chen Jieh-Haur¹²,Yang Li-Ren³,Wang Jui-Pin¹,Lin Shang-I⁴,Cheng Jiun-Yao²,Lee Meng-Hsueh⁵,Chen Chih-Lin¹

Affiliation:

1. Department of Civil Engineering, National Central University, Jhongli, Taoyuan 32001, Taiwan.

2. Research Center of Smart Construction, National Central University, Jhongli, Taoyuan 32001, Taiwan.

3. Department of Business Administration, Tamkang University, Tamsui, Taipei 25137, Taiwan.

4. Public Construction Commission, Executive Yuan, Taipei 11010, Taiwan.

5. Center for Weather Climate and Disaster Research, National Taiwan University, Taipei 10617, Taipei.

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.

Publisher

Canadian Science Publishing

Subject

General Environmental Science,Civil and Structural Engineering

Link

https://cdnsciencepub.com/doi/pdf/10.1139/cjce-2019-0561

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