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An Integrated Sensor-Fusion Framework for Performance-Based Compensation in Construction
Construction productivity has historically stagnated due to a reliance on subjective labor monitoring and “lagging” performance indicators. This research proposes a novel compensation model with the use of Total Energy Management System (TEMS) to translate labor effort and efficiency into payment in...
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| Format: | Thesis |
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AUC Knowledge Fountain
2026
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| Summary: | Construction productivity has historically stagnated due to a reliance on subjective labor monitoring and “lagging” performance indicators. This research proposes a novel compensation model with the use of Total Energy Management System (TEMS) to translate labor effort and efficiency into payment incentives with the integration of Internet of Things (IoT) wearables and Statistical Process Control (SPC) charts.
This research addresses the systemic problem of declining construction productivity caused by industry’s inability to objectively quantify human effort. While traditional models rely on post-facto production tallies, a significant research gap exists in real-time physiological labor monitoring. To bridge this, the proposed TEMS model serves as a dual-purpose objective: to establish a biological baseline for labor efficiency and to create a transparent incentive structure. The methodology employs a high-frequency IoT data-collection strategy, utilizing sensors to generate the 'Blackwell Signal' necessary for SPC analysis. The application of this framework demonstrates that by normalizing metabolic costs against mechanical work, management can shift from a focus on raw exertion to optimized 'Lean' output. The results conclude that this system not only identifies high-value performers but also provides a predictive safeguard against physical overburden, offering a sustainable path toward 'Construction 4.0' where compensation is mathematically aligned with both human well-being and industrial efficacy.
Workers and method statements were divided into four quadrants using a dual-axis classification matrix: Lean Expert, Powerhouse, Struggler, and Underperformer. By using a physics-based study of Force and Distance to normalize human metabolic energy (kcal) against mechanical energy (Watt-hours), this model is further extended to machinery. The findings show that machine-assisted techniques separate high production from physical overburden (Muri), enabling employees to sustain "Lean Expert" status with reduced calorie waste. This concept offers a statistically sound basis for a performance-based compensation system that prioritizes "smart work" over "hard work," ultimately providing a scalable way to boost workers’ well-being and industry-wide productivity |
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