TY - JOUR
T1 - Learning-driven construction productivity prediction for prefabricated external insulation wall system
AU - Jeong, Jaemin
AU - Jeong, Jaewook
AU - Lee, Jaehyun
AU - Kim, Daeho
AU - Son, Jeong Wook
N1 - Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/9
Y1 - 2022/9
N2 - 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.
AB - 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.
KW - Activity cycle diagrams
KW - Construction productivity
KW - K-fold-cross validation
KW - Machine learning
KW - Prefabricated external insulation wall
UR - http://www.scopus.com/inward/record.url?scp=85132746837&partnerID=8YFLogxK
U2 - 10.1016/j.autcon.2022.104441
DO - 10.1016/j.autcon.2022.104441
M3 - Article
AN - SCOPUS:85132746837
SN - 0926-5805
VL - 141
JO - Automation in Construction
JF - Automation in Construction
M1 - 104441
ER -