Construction Worker Safety Prediction and Active Warning Based on Computer Vision and the Gray Absolute Decision Analysis Method
Publication: Journal of Construction Engineering and Management
Volume 149, Issue 4
Abstract
Although a great deal of worker safety risks analysis has been conducted, safety accidents continue to occur and recur at regular intervals. On construction sites, various activities have different levels of accident probability and severity, and existing methods are limited because they involve the use of the same weights to assess accident probability and accident severity. In addition, uncertainties that occur over time result in rapidly changing risks. The majority of existing approaches provide postevent warnings, so workers may not have sufficient time to prevent accidents once they are warned about the possibility. Thus, there is a need for mechanisms that can be used to assess safety risk regularity, predict worker risk levels, and provide proactive warnings based on the comprehensive consideration of the impacts of worker behaviors and environments. To address these issues, a safety prediction model was proposed for use, and an active warning mechanism was constructed for construction workers. The prediction model performs accident potential regularity analysis based on attribute-based safety risk analysis and precursor analysis. Further, it quantifies worker risk levels using decision matrix risk assessment (DMRA) and the gray absolute decision analysis (GADA) method. The model overcomes the limitation of using the same weights in DMRA to assess accident probability and severity. An active warning mechanism for construction workers was created to validate the efficacy of the safety prediction model, and the proposed safety prediction model is embedded in the mechanism. The mechanism mainly consists of three modules: (1) a data collection module that mainly includes expert knowledge and dynamic safety information from surveillance cameras; (2) a data analysis module that mainly uses the proposed safety prediction model to predict individual worker risk levels; and (3) an early warning module that displays the predicted risk levels, dynamically ranks risk indicators, and provides corresponding early warning measures. Finally, the feasibility and operability of the proposed active warning mechanism are demonstrated through a practical case study.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
This research was supported in part by the National Science and Technology Council (NSTC) of Taiwan under Grant NSTC 111-2222-E-992-007.
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Published In
Journal of Construction Engineering and Management
Volume 149 • Issue 4 • April 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: May 13, 2022
Accepted: Dec 1, 2022
Published online: Feb 10, 2023
Published in print: Apr 1, 2023
Discussion open until: Jul 10, 2023
ASCE Technical Topics:
- Accidents
- Business management
- Computer vision and image processing
- Construction engineering
- Construction management
- Construction methods
- Disaster risk management
- Employment
- Engineering fundamentals
- Labor
- Mathematics
- Methodology (by type)
- Occupational safety
- Personnel management
- Practice and Profession
- Probability
- Public administration
- Public health and safety
- Risk management
- Safety
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