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Article: Knowledge graph-enabled adaptive work packaging approach in modular construction

TitleKnowledge graph-enabled adaptive work packaging approach in modular construction
Authors
KeywordsConstruction management
Deep learning
Industralized construction
Knowledge graph
Work packaging
Issue Date2023
Citation
Knowledge-Based Systems, 2023, v. 260, article no. 110115 How to Cite?
AbstractAdaptive work packaging is paramount in helping reduce dynamic gaps between design and manufacturing in modular construction (MC), particularly in mass customization. However, current work packaging methods fail to automatically extract complex semantic relations among work package elements (e.g., products, tasks, and their dependencies) and dynamically reason the implicit semantic knowledge (e.g., the different granularity of semantics) as the project progresses. To address these issues, this study proposes a knowledge graph-enabled adaptive work packaging (K-GAWP) approach to dynamically form semantic-enriched work packages with different granularities. Thus far, this study first models the data of tasks, products, and their spatial relationships for MC production as graphs. Second, a novel multi-granularity knowledge reasoning method (product2task) is developed to map products to tasks in an adaptive manner. Third, a dedicated hierarchical clustering method (task2package) involving multiple features from the dependency structure matrix is proposed for work-package generation (i.e., task knowledge fusion). Finally, the K-GAWP's performance is evaluated through controlled experiments in a real MC project. The results indicate that the K-GAWP approach performs work packaging in an adaptive, accurate, and efficient manner, thereby improving the distributed planning and control of MC projects.
Persistent Identifierhttp://hdl.handle.net/10722/326374
ISSN
2023 Impact Factor: 7.2
2023 SCImago Journal Rankings: 2.219
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLi, Xiao-
dc.contributor.authorWu, Chengke-
dc.contributor.authorYang, Zhile-
dc.contributor.authorGuo, Yuanjun-
dc.contributor.authorJiang, Rui-
dc.date.accessioned2023-03-09T10:00:11Z-
dc.date.available2023-03-09T10:00:11Z-
dc.date.issued2023-
dc.identifier.citationKnowledge-Based Systems, 2023, v. 260, article no. 110115-
dc.identifier.issn0950-7051-
dc.identifier.urihttp://hdl.handle.net/10722/326374-
dc.description.abstractAdaptive work packaging is paramount in helping reduce dynamic gaps between design and manufacturing in modular construction (MC), particularly in mass customization. However, current work packaging methods fail to automatically extract complex semantic relations among work package elements (e.g., products, tasks, and their dependencies) and dynamically reason the implicit semantic knowledge (e.g., the different granularity of semantics) as the project progresses. To address these issues, this study proposes a knowledge graph-enabled adaptive work packaging (K-GAWP) approach to dynamically form semantic-enriched work packages with different granularities. Thus far, this study first models the data of tasks, products, and their spatial relationships for MC production as graphs. Second, a novel multi-granularity knowledge reasoning method (product2task) is developed to map products to tasks in an adaptive manner. Third, a dedicated hierarchical clustering method (task2package) involving multiple features from the dependency structure matrix is proposed for work-package generation (i.e., task knowledge fusion). Finally, the K-GAWP's performance is evaluated through controlled experiments in a real MC project. The results indicate that the K-GAWP approach performs work packaging in an adaptive, accurate, and efficient manner, thereby improving the distributed planning and control of MC projects.-
dc.languageeng-
dc.relation.ispartofKnowledge-Based Systems-
dc.subjectConstruction management-
dc.subjectDeep learning-
dc.subjectIndustralized construction-
dc.subjectKnowledge graph-
dc.subjectWork packaging-
dc.titleKnowledge graph-enabled adaptive work packaging approach in modular construction-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.knosys.2022.110115-
dc.identifier.scopuseid_2-s2.0-85143523894-
dc.identifier.volume260-
dc.identifier.spagearticle no. 110115-
dc.identifier.epagearticle no. 110115-
dc.identifier.isiWOS:000907728200002-

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