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

In: Proceedings of the 26th International Symposium on Advancement of Construction Management and Real Estate

Author

Listed:
  • 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)

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.

Suggested Citation

  • Xiao Li & Chengke Wu & Weisheng Lu & Fan Xue, 2022. "Latent Dirichlet Allocation-Based Approach for Automatically Mapping Components to Tasks in Modular Construction," Lecture Notes in Operations Research, in: Hongling Guo & Dongping Fang & Weisheng Lu & Yi Peng (ed.), Proceedings of the 26th International Symposium on Advancement of Construction Management and Real Estate, pages 1133-1145, Springer.
    • Handle: RePEc:spr:lnopch:978-981-19-5256-2_89
    DOI: 10.1007/978-981-19-5256-2_89
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