Industrialized building construction is an approach that integrates manufacturing techniques into construction projects to achieve improved quality, shortened project duration, and enhanced schedule predictability. Time savings result from concurrently carrying out factory operations and site preparation activities. In an industrialized building construction factory, the accurate prediction of production cycle time is crucial to reap the advantage of improved schedule predictability leading to enhanced production planning and control. With the large amount of data being generated as part of the daily operations within such a factory, the present study proposes a machine learning approach to accurately estimate production time using (1) the physical characteristics of building components, (2) the real-time tracking data gathered using a radio frequency identification system, and (3) a set of engineered features constructed to capture the real-time loading conditions of the job shop. The results show a mean absolute percentage error and correlation coefficient of 11% and 0.80, respectively, between the actual and predicted values when using random forest models. The results confirm the significant effects of including shop utilization features in model training and suggest that predicting production time can be reasonably achieved.
Bibliographical noteFunding Information:
This research was funded through a Collaborative Research Development Grant by the Natural Science and Engineering Research Council of Canada (Grant File No. CRDPJ 536466-18).
© 2022 Elsevier Ltd
- Industrialized building construction
- Machine learning
- Prefabricated construction
- Production time
- Time prediction
ASJC Scopus subject areas
- Information Systems
- Artificial Intelligence