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|Title:||SITUATIONAL AWARENESS FOR CONSTRUCTION SAFETY RISKS MANAGEMENT|
|Abstract:||The high number of construction injuries and fatalities resulted from struck-by-equipment hazards is one of the major challenges faced by the construction industry. Improved situational awareness assists workers to recognize hazardous situations, make decisions, and take actions in a timely manner to prevent hazards. Advanced technologies have been widely recognized as holding great promise to enable innovative applications in construction to improve situational awareness and to prevent struck-by-equipment hazards. However, existing solutions for detecting struck-by-equipment hazards generate frequent false alarms which interrupt construction and reduce site mobility and productivity. Also, there has been a need for an integrated method that can concurrently monitor and analyze the struck-by-equipment risk at both individual and system levels to enable proactive hazard prevention. This research addresses the above-mentioned challenges by introducing the situational awareness for construction safety risks management (SA4SR), realizing timely and accurate hazard detection and dynamic risk analysis. Accordingly, the SA4SR consists of two modules: hazard detection and risk awareness. The hazard detection module focuses on identifying safety hazards in near real time with reduced false alarms. Three unsafe-proximity detection models were developed, which can not only identify struck-by-equipment hazards in a timely manner but also reduce false alarms. The effectiveness of these three models in reducing false alarms was evaluated and confirmed in both simulation and field experiments. The risk awareness module is centered on analyzing safety risk levels over time for individual entities and the whole construction sites. A spatiotemporal network-based model with three safety leading indicators was developed to analyze the struck-by-equipment risk at both entity and network levels. The risk analysis at entity and network levels was conducted using four simulated sites, and the derived safety applications were summarized. The developed SA4SR addressed the limitations of existing proximity detection methods and further developed a dynamic risk analysis model to comprehensively analyze struck-by-equipment risk. The situational awareness is improved by applying the developed models in the SA4SR to analyze sensed data (e.g., motions of entities). Consequently, hazards can be identified, risk evolution can be tracked and analyzed, and safety performance can be evaluated and compared. The developed SA4SR is expected to alleviate safety concerns in the construction industry and also can be extended to other types of contact collisions on sites to further enhance safety.|
|Appears in Collections:||Open Access Dissertations and Theses|
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|WANG_JUN_2018FEB_PHD.pdf||3.68 MB||Adobe PDF||View/Open|
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