Construction has a higher injury and fatality rate than most of the other industries. Given this situation, existing research has studied various issues and factors affecting construction safety management and has attempted to use all available methods to improve the construction safety performance. However, the construction accident rate remains among the highest in the United States and the world. The primary objective of this research is to advance autonomous proximity monitoring and hence provide a safer environment for construction workers. In particular, I seek to advance current evaluations of proximity warning technologies to a more robust engineering approach to the design and deployment of autonomous safety monitoring systems. The contributions of the research are demonstrated through specifications, deployments, and testing of proximity monitoring systems for crane loads and falling from height. My research advances current knowledge in three areas. First, I develop specifications for proximity safety monitoring in a sensed environment, built from existing guidelines and expert interviews. Second, I translate the specifications to computer interpretable rules and deploy them in a distributed computing environment. This demonstrates the feasibility of a systems approach and reusability of components to speed deployment. Third, I evaluate the accuracy of the specifications and systems under imperfect data. I further evaluate some approaches to dealing with imperfect data. Collectively, these advances move existing proximity warning research from evaluation of specific systems to an engineering approach to development and deployment of distributed systems with reusable components that explicitly treats imperfect data. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/20912 |
| Date | 24 July 2013 |
| Creators | Luo, Xiaowei |
| Source Sets | University of Texas |
| Language | en_US |
| Detected Language | English |
| Format | application/pdf |
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