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

doi:10.1139/cjce-2019-0561

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 journal › Article › peer-review

7 Scopus citations

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 language	English
Pages (from-to)	1020-1025
Number of pages	6
Journal	Canadian Journal of Civil Engineering
Volume	48
Issue number	8
DOIs	https://doi.org/10.1139/cjce-2019-0561
State	Published - 2021

Keywords

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

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10.1139/cjce-2019-0561

Cite this

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title = "Automatic manpower allocation for public construction projects using a rough set enhanced neural network",

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.",

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AU - Chen, Jieh Haur

AU - Yang, Li Ren

AU - Wang, Jui Pin

AU - Lin, Shang I.

AU - Cheng, Jiun Yao

AU - Lee, Meng Hsueh

AU - Chen, Chih Lin

PY - 2021

Y1 - 2021

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AB - 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.

KW - Artificial neural network (ANN)

KW - Construction project

KW - Manpower

KW - Prediction

KW - Rough set

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

Abstract

Keywords

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