Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105393
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Building and Real Estate | - |
dc.creator | Chen, JH | - |
dc.creator | Chen, CL | - |
dc.creator | Wei, HH | - |
dc.date.accessioned | 2024-04-12T06:52:11Z | - |
dc.date.available | 2024-04-12T06:52:11Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/105393 | - |
dc.language.iso | en | en_US |
dc.publisher | Molecular Diversity Preservation International (MDPI) | en_US |
dc.rights | © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Chen J-H, Chen C-L, Wei H-H. Manpower Allocation of Work Activities for Producing Precast Components: Empirical Study in Taiwan. Sustainability. 2023; 15(9):7436 is available at https://doi.org/10.3390/su15097436. | en_US |
dc.subject | Construction management | en_US |
dc.subject | K-Nearest Neighbor | en_US |
dc.subject | Manpower allocation | en_US |
dc.subject | Precast component | en_US |
dc.subject | Rough set | en_US |
dc.title | Manpower allocation of work activities for producing precast components : empirical study in Taiwan | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 15 | - |
dc.identifier.issue | 9 | - |
dc.identifier.doi | 10.3390/su15097436 | - |
dcterms.abstract | The production of precast components in the construction industry is a labor-intensive process. The objectives of this study are to prove the feasibility of using rough set theory to classify and weigh impact attributes, and to develop a model to assess the total quantities of labor needed for precast structural elements using a rough set enhanced K-Nearest Neighbor (KNN). Three main building components (beams, girders, and columns) were collected from the production of precast elements in Taiwan. After trimming and analyzing the basic data, the rough set approach is used to classify and weight the attributes into three levels of impact based on their frequency. A rough set enhanced KNN is accordingly developed, yielding an accuracy rate of 92.36%, which is 8.09% higher than the result obtained when using the KNN algorithm. A practical and effective prediction model would assist managers to estimate the manpower requirement of precast projects. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Sustainability, May 2023, v. 15, no. 9, 7436 | - |
dcterms.isPartOf | Sustainability | - |
dcterms.issued | 2023-05 | - |
dc.identifier.scopus | 2-s2.0-85159342825 | - |
dc.identifier.eissn | 2071-1050 | - |
dc.identifier.artn | 7436 | - |
dc.description.validate | 202403 bcvc | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.fundingSource | others | en_US |
dc.description.fundingText | Ministry of Science and Technology of Taiwan | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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sustainability-15-07436.pdf | 1.54 MB | Adobe PDF | View/Open |
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