A machine learning approach to predict production time using real-time RFID data in industrialized building construction |
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| Affiliation: | 1. College of Civil Engineering, Central South University, Changsha 410075, China;2. State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha 410083, China;3. China Railway Construction Heavy Industry Co. Ltd, Changsha 410100, China;4. Key Laboratory of Shield Tunneling and Tunneling Tool Technology in Jilin Province, Jilin Welter Tunnel Equipment Co., Ltd, Jilin 132299, China;1. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China;2. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China;3. State Key Laboratory for Manufacturing Systems Engineering, Xi ’an Jiaotong University, Xi''an, 710049, China;1. School of Mechanical Engineering, Yanshan University, Qinhuangdao City, Hebei, PR China;2. Department of Mechanical Engineering, University of Manitoba, Winnipeg, MB, Canada;1. Information Systems and Engineering (CIISE), Concordia University, Canada;2. Department of Industrial and Systems Engineering, Wayne State University, Detroit, MI 48202, USA |
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| Abstract: | 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. |
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| Keywords: | Industrialized building construction Prefabricated construction Production time Time prediction RFID Machine learning |
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