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Sensor-Based Safety Performance Assessment of Individual Construction Workers.

JeeWoong Park1, Yong K Cho2, Ali Khodabandelu3

  • 1Department of Civil and Environmental Engineering and Construction, The University of Nevada, Las Vegas, NV 89154, USA. jee.park@unlv.edu.

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This study introduces a new safety framework using worker location data to quantify individual safety performance on construction sites. The model provides a safety index, improving upon traditional detection and warning systems.

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Area of Science:

  • Construction Safety Engineering
  • Occupational Health and Safety
  • Human-Computer Interaction

Background:

  • Worker safety in construction sites is a critical concern, with existing technologies primarily focused on detection and warning systems.
  • Limited research exists on quantitatively assessing individual workers' safety performance beyond simple detection.
  • Current safety monitoring methods are often costly, error-prone, and time-consuming due to reliance on human deployment.

Purpose of the Study:

  • To develop and validate a novel computational framework for quantitatively assessing individual worker safety performance.
  • To move beyond basic safety issue detection towards a more nuanced, performance-based safety evaluation.
  • To introduce a quantifiable safety index for construction workers, enhancing safety management.

Main Methods:

  • Utilized a tracking system to collect individual worker location data within a proposed safety framework.
  • Developed a computational and analytical model to quantify safety performance based on location and zone-based risks.
  • Defined parameters for zone-based safety risks and established a corresponding risk model.
  • Conducted field tests at construction sites with various interaction scenarios to demonstrate the model.

Main Results:

  • The developed model successfully quantified individual worker safety performance, capturing conditions in pre-identified hazard zones.
  • Probabilistic evaluations indicated generally accurate performance representation, with minor under/overestimations in specific instances.
  • The approach provides visualized and quantified safety information, creating a novel safety index for the industry.

Conclusions:

  • The proposed framework offers an automated and quantitative method for assessing worker safety performance, surpassing traditional detection systems.
  • This approach can significantly enhance construction site safety monitoring by providing objective, data-driven insights.
  • The development of a safety index represents a significant advancement, potentially reducing reliance on manual safety inspections.