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Computer Vision-Based Work Productivity Optimization with Lean Principles and Simulation Method

  • 2025
  • OriginalPaper
  • Chapter
Published in:
Authors
Xue Chen
William Correa
Rafik Lemouchi
Alejandro Ramon
Mohammad Masfiqul Alam Bhuiyan
Mohamed Al-Hussein
Published in
Proceedings of the Canadian Society for Civil Engineering Annual Conference 2024, Volume 8
Publisher
Springer Nature Switzerland

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Abstract

This study delves into the application of lean principles and computer vision technology to optimize work productivity in construction. By combining these methods with simulation techniques, the research identifies significant improvements in efficiency and resource utilization. The investigation focuses on the traditional manufacturing enterprise's production line, where lean principles are applied to eliminate waste and enhance productivity. Computer vision technology, utilizing models like YOLO-V5 and Mask R-CNN, automates the collection of work information, replacing time-consuming manual observations. The study builds a simulation model with Simphony to compare traditional and lean construction scenarios, highlighting a 24% reduction in total duration and an 88% increase in effectiveness. Key topics include the integration of lean principles, the role of computer vision in data collection, and the impact of simulation models on construction productivity. The conclusion emphasizes the potential of combining these technologies to achieve significant improvements in construction efficiency.
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previous chapter Assessment of the Impact of Climate Change on Thunderstorm Winds
next chapter Digital As-built Documentation for Buildings: How Different Data Capturing Technologies Enable 3D Model Reconstruction and BIM Enrichment
Title
Computer Vision-Based Work Productivity Optimization with Lean Principles and Simulation Method
Authors
Xue Chen
William Correa
Rafik Lemouchi
Alejandro Ramon
Mohammad Masfiqul Alam Bhuiyan
Mohamed Al-Hussein
Copyright Year
2025
Book
Proceedings of the Canadian Society for Civil Engineering Annual Conference 2024, Volume 8

Print ISBN: 978-3-031-95420-7

Electronic ISBN: 978-3-031-95421-4

Copyright Year: 2025

DOI
https://doi.org/10.1007/978-3-031-95421-4_7
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