A Sensor-Based Empirical Framework to Measure Construction Labor Productivity
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Nguyen, P. H. D., Fayek, A. Robinson, & Hamzeh, F. (2022). A sensor-based empirical framework to measure construction labor productivity. Proceedings, Construction Research Conference 2022, Arlington, VA, USA, March 7–10. doi.org/10.1061/9780784483961.001.
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Abstract
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Measurement of construction labor productivity involves various subjective factors (e.g., motivation, stress, and fatigue). Most measurement approaches for subjective factors in productivity applications require manual data collection (e.g., questionnaires, interviews, and observations); therefore, research gaps exist regarding how to (1) directly measure subjective factors using data that reflect workers’ real performance at single points in time, and (2) integrate these factors into existing or new models in labor productivity applications. This paper proposes an empirical framework for integrating real-time data from multiple sensors for directly measuring subjective factors affecting labor productivity. The proposed framework, which was designed, built, and evaluated using design science research methodology, contributes to the body of knowledge as part of a longer-term study proposing an empirical framework for triangulating data from a multi-sensor system to simultaneously measure multiple subjective factors affecting labor productivity. Study outcomes will complement existing artificial intelligence, simulation, and statistical models for construction productivity applications.
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http://purl.org/coar/resource_type/R60J-J5BD
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This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil Engineers. This material may be found at https://ascelibrary.org/doi/10.1061/9780784483978.076.
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en