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Knowledge graph-enabled adaptive work packaging approach in modular construction
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PRE
AI
DOI：10.1016/j.knosys.2022.110115.png)
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摘要
En 中文
Adaptive work packaging is paramount in helping reduce dynamic gaps between design and manu-facturing 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.(c) 2022 Elsevier B.V. All rights reserved.
Keyword:
Work packaging
Industralized construction
Construction management
Knowledge graph
Deep learning
期刊
K
IF:
7.6
论文数:
1.2W
被引数:
4.5W
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