The International Association for Automation and Robotics in Construction
Publications / 2025 Proceedings of the 42nd ISARC, Montreal, Canada
Non-Productive Time Reduction in Off-Site Construction: A Predictive Analytics Approach Using Deep Learning
This study proposes a data-driven framework to reduce Non-Productive Time (NPT) in Off-Site Construction (OSC) by integrating predictive modeling with lean principles. Through comprehensive computer vision (CV) techniques, large-scale video data are analyzed to identify key operational states, worker positions, and material flows. Two primary classification approachesa Random Forest (RF) model and a Deep Learning (DL) networkare employed to detect NPT episodes. Experimental results reveal that the DL model achieves substantially higher accuracy and F1 scores, demonstrating its capability to handle complex spatialtemporal interactions. Feature-weight analysis further indicates that task transitions, assembly processes, and coordinate-based congestion zones are critical drivers of NPT. Building on these insights, targeted lean interventionssuch as just-intime material delivery, proactive maintenance, and optimized workstation layoutscan streamline operations, minimize idle intervals, and enhance overall productivity. The conclusions underscore the effectiveness of merging data analytics with lean strategies in uncovering hidden inefficiencies within OSC, providing a clear direction for future research and industry adoption.
