SmartHat: Self-Monitoring, Analysis, and Reporting Technology for Hazard Avoidance and Training
SmartHat: Self-Monitoring, Analysis, and Reporting Technology for Hazard Avoidance and Training
批准号:
0800858
负责人:
Jochen Teizer
金额:
$27.23万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2008
资助国家:
美国
项目状态:
已结题
起止时间:
2008-07-01 至 2012-06-30
中文摘要
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英文摘要
Safety first! Safety is the first goal in any work process. To finish a construction project, resources such as workers, large machines, and materials are needed. These resources frequently move from one location to another location on the construction site. In this process, however, some very common accidents occur. The most common of these include falls from higher levels, e.g. workers falling from scaffolding or into holes in the ground, or workers being hit by larger machines. Overall, for the last 13 years the United States construction industry has experienced over 1,000 fatalities annually, accounting for more than 25% of all industry workplace fatalities. The problem of safety is much bigger when considering developing countries such as India and China, where construction is proceeding at a frantic pace. To solve it, this research aims to improve construction worker safety by creating methods to integrate new sensing technologies on the job site that can automatically detect and warn workers early enough before getting hurt. These technologies include optical systems, such as cameras or lasers, and wireless systems, such as radio based tagging for detection and tracking of workers, machines, and materials. This research will establish automated site monitoring and recording as an integral part of construction safety management. Additionally, this research will also provide a new set of tools for training construction workers and construction educators in best practices for worker safety at today's increasingly complex construction sites. This research will lead to new training materials and practices that will have a direct impact on worker safety. Because of the inherent diversity of the construction labor pool, this research will have broad impact on labor safety and training for minority groups as well. This research will actively include students with interest in civil, electrical, and computational engineering.
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