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Article: Ontology-based mapping approach for automatic work packaging in modular construction

TitleOntology-based mapping approach for automatic work packaging in modular construction
Authors
Issue Date2022
Citation
Automation in Construction, 2022, v. 134, p. 104083 How to Cite?
AbstractMany cross-knowledge domain tasks involving various professional backgrounds have been transferred from construction sites to factories in modular construction (MC). In MC, forming optimal work packages which can handle the complexity of product breakdown structures and dynamic project progress is critical for task planning and execution. However, forming MC work packages is time-consuming and ineffective because it is performed manually while not adequately considering domain knowledge. To address the problem, this study proposes a dynamic ontology-based mapping (DOM) approach to automatically generate semantic-enriched work packages. For this purpose, ontologies of MC products, topology, and tasks are established to incorporate domain knowledge. Then, a customized Latent Dirichlet Allocation (LDA) model for mapping products to tasks and a weighted hierarchical clustering model for grouping dynamic tasks into work packages are developed. The effectiveness of the DOM approach is tested in an MC case project and controlled experiments. The results demonstrate that the DOM approach can significantly increase the accuracy and efficiency of the dynamic work packaging process while reducing planning time compared to conventional methods, which thus improve the collaborative management and performance of MC projects.
Persistent Identifierhttp://hdl.handle.net/10722/312320
ISSN
2023 Impact Factor: 9.6
2023 SCImago Journal Rankings: 2.626
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLi, X-
dc.contributor.authorWu, C-
dc.contributor.authorXue, F-
dc.contributor.authorYang, Z-
dc.contributor.authorLOU, J-
dc.contributor.authorLu, WW-
dc.date.accessioned2022-04-25T01:38:08Z-
dc.date.available2022-04-25T01:38:08Z-
dc.date.issued2022-
dc.identifier.citationAutomation in Construction, 2022, v. 134, p. 104083-
dc.identifier.issn0926-5805-
dc.identifier.urihttp://hdl.handle.net/10722/312320-
dc.description.abstractMany cross-knowledge domain tasks involving various professional backgrounds have been transferred from construction sites to factories in modular construction (MC). In MC, forming optimal work packages which can handle the complexity of product breakdown structures and dynamic project progress is critical for task planning and execution. However, forming MC work packages is time-consuming and ineffective because it is performed manually while not adequately considering domain knowledge. To address the problem, this study proposes a dynamic ontology-based mapping (DOM) approach to automatically generate semantic-enriched work packages. For this purpose, ontologies of MC products, topology, and tasks are established to incorporate domain knowledge. Then, a customized Latent Dirichlet Allocation (LDA) model for mapping products to tasks and a weighted hierarchical clustering model for grouping dynamic tasks into work packages are developed. The effectiveness of the DOM approach is tested in an MC case project and controlled experiments. The results demonstrate that the DOM approach can significantly increase the accuracy and efficiency of the dynamic work packaging process while reducing planning time compared to conventional methods, which thus improve the collaborative management and performance of MC projects.-
dc.languageeng-
dc.relation.ispartofAutomation in Construction-
dc.titleOntology-based mapping approach for automatic work packaging in modular construction-
dc.typeArticle-
dc.identifier.emailXue, F: xuef@hku.hk-
dc.identifier.emailLu, WW: wilsonlu@hku.hk-
dc.identifier.authorityXue, F=rp02189-
dc.identifier.authorityLu, WW=rp01362-
dc.identifier.doi10.1016/j.autcon.2021.104083-
dc.identifier.scopuseid_2-s2.0-85120815026-
dc.identifier.hkuros332765-
dc.identifier.volume134-
dc.identifier.spage104083-
dc.identifier.epage104083-
dc.identifier.isiWOS:000740339100004-

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