Learning-driven construction productivity prediction for prefabricated external insulation wall system
- 주제(키워드) Activity cycle diagrams , Construction productivity , K-fold-cross validation , Machine learning , Prefabricated external insulation wall
- 등재 SCIE, SCOPUS
- 발행기관 Elsevier B.V.
- 발행년도 2022
- URI http://www.dcollection.net/handler/ewha/000000193250
- 본문언어 영어
- Published As https://doi.org/10.1016/j.autcon.2022.104441
초록/요약
The recent shortage of young skilled laborers is one of the impending issues facing the global construction industry. To address these issues, the prefabricated external insulation system (PEIS) can be suggested as an alternative. However, before applying it to construction projects, a construction productivity analysis is difficult due to the complexity of simulation modeling and the absence of real data. Thus, this paper aims to develop a learning-based productivity prediction model for PEIS using machine learning. This describes a learning-based productivity prediction model for PEIS using machine learning and consists of three steps: (i) Establishment of data, (ii) Development of activity cycle diagram for PEIS, and (iii) Prediction model for productivity analysis. The prediction model has a precision rate of 99.09%. This paper contributes to the literature by developing the possibility of a quick analysis of construction productivity without real data through a machine learning approach. © 2022 Elsevier B.V.
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