Digital Twin for Production Estimation, Scheduling and Real-Time Monitoring in Offsite Construction
Creators: Alsakka, Fatima
Publisher: name: University of Alberta Library
Year: 2023

<p>The offsite construction industry continues to rely on experience-based average production rates (i.e., average quantity per unit of time) to estimate and schedule production operations. This approach is hindered by various sources of production variability, such as machine breakdowns and material shortages, often resulting in high production estimation and scheduling errors; in fact, as described herein, using average production rates may result in overly optimistic production schedules, leading to missing schedule deadlines, cost overruns, and, most critically, an overburdened workforce. In this context, this thesis proposes a digital twin to enable dynamic production estimation, scheduling, and real-time monitoring of production operations in offsite construction with more accuracy compared to the current practice. The proposed digital twin comprises three major subsystems: (1) an estimation and scheduling subsystem, which estimates variable cycle times as a function of various factors that influence them and virtually mimics operations to estimate production time and generate production schedules; (2) a computer-vision-based data acquisition subsystem that enables the continuous collection of data necessary for regular tuning of the estimation models, accommodating new sources of variability; and (3) a real-time monitoring subsystem to monitor production operations in real time, tracking progress on production schedules and enabling the generation of updated schedules promptly in response to any deviations from the actual operations.<br>
To support the development of these subsystems and their requisite functionalities, four main research objectives are pursued: (1) develop and examine a system that deploys computer-vision technology for the automated and accurate acquisition of cycle time data in a timely and cost-effective manner; (2) devise a methodical approach for the identification and understanding of the factors driving cycle time variability, and evaluate how this identification process improves the accuracy of cycle time estimation; (3) design and develop a data- and knowledge-driven system that estimates cycle times in consideration of various influencing factors and using automatically collected data to increase the estimation accuracy compared to traditional estimation methods; and (4) devise a feasible design of a digital twin that enables dynamic and more accurate production estimation, scheduling, and real-time monitoring in offsite construction factories. A diverse array of methods and technologies, including computer vision, 3D simulation, machine-learning-based prediction, statistical modelling, ultrasonic sensors, semi-structured interviews, direct observation, and literature reviews, are deployed and integrated to achieve these objectives.<br>
A prototype of the digital twin is developed for a wall framing workstation within a panelized construction factory. The results show that average errors of less than 1 minute in data acquisition, a 36% reduction in cycle time estimation errors, and an 81% reduction in deviations between the production schedule and actual production are achieved compared to the current practice of relying on experience-based average production rates.</p>