A Multi-Modal ML Framework for Construction Labor Productivity Optimization: Computer Vision, Predictive Analytics, and Reinforcement Learning Applications

Author(s)	Sai Kothapalli
Country	United States
Abstract	The construction industry faces persistent challenges in labor productivity, with studies indicating minimal improvement over the past decades. This paper presents a comprehensive analysis of machine learning (ML) applications for enhancing construction labor productivity. This research examines various ML techniques including computer vision, predictive analytics, and optimization algorithms applied to productivity monitoring, workforce planning, and task allocation. Through analysis of three case studies from major construction projects, this research demonstrates productivity improvements ranging from 15% to 32%. This research findings indicate that ML-based systems can significantly enhance labor productivity through real-time monitoring, predictive maintenance, and optimized resource allocation. The paper concludes with recommendations for implementation strategies and future research directions.
Field	Engineering
Published In	Volume 5, Issue 6, November-December 2023
Published On	2023-12-08
DOI	https://doi.org/10.36948/ijfmr.2023.v05i06.47565
Short DOI	https://doi.org/g9q3zc

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International Journal For Multidisciplinary Research