TY - JOUR
T1 - Ontology-based mapping approach for automatic work packaging in modular construction
AU - Li, Xiao
AU - Wu, Chengke
AU - Xue, Fan
AU - Yang, Zhile
AU - Lou, Jinfeng
AU - Lu, Weisheng
N1 - Funding Information:
This research was supported by the Start-up Fund of The Hong Kong Polytechnic University (No. A0039232) and Fellowship of China Postdoctoral Science Foundation (No. 2021M692169). The work was also partially supported by a fellowship award from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. PDFS2021-7S10).
Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2022/2
Y1 - 2022/2
N2 - Many 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.
AB - Many 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.
KW - Hierarchical clustering
KW - Latent Dirichlet allocation
KW - Modular construction
KW - Ontology
KW - Project planning
KW - Work package
UR - http://www.scopus.com/inward/record.url?scp=85120815026&partnerID=8YFLogxK
U2 - 10.1016/j.autcon.2021.104083
DO - 10.1016/j.autcon.2021.104083
M3 - Journal article
AN - SCOPUS:85120815026
SN - 0926-5805
VL - 134
JO - Automation in Construction
JF - Automation in Construction
M1 - 104083
ER -