Latent Dirichlet Allocation-Based Approach for Automatically Mapping Components to Tasks in Modular Construction

Xiao Li (), Chengke Wu (), Weisheng Lu and Fan Xue
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Xiao Li: The University of Hong Kong
Chengke Wu: Chinese Academy of Sciences
Weisheng Lu: The University of Hong Kong
Fan Xue: The University of Hong Kong

A chapter in Proceedings of the 26th International Symposium on Advancement of Construction Management and Real Estate, 2022, pp 1133-1145 from Springer

Abstract: Abstract A large portion of cross-knowledge domain tasks have interdependent relationships with varied components in modular construction (MC). The MC components serve as the critical resources to support the task planning and execution for generating excellent MC products and services. Meanwhile, dynamic changes of tasks may adversely affect the design, procurement, and assembly of components. Furthermore, manually mapping components to tasks will be time-consuming and prevent forming effective work packages to achieve collaborative working. Thus, this study aims to develop an approach for automatically connecting components with tasks, which helps workers efficiently know the relationships between tasks and components. To this end, the latent Dirichlet allocation (LDA) approach is customized to this task-component mapping scenario. Moreover, compared with other leading unsupervised clustering techniques, e.g., K-means, the customized LDA demonstrated better performance on accuracy and efficiency for task-component mapping, and it can pave the way for effective work package formation in MC.

Keywords: Modular construction; Latent Dirichlet allocation; Components; Tasks; Work package (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/978-981-19-5256-2_89

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