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SAVES: A AUGMENTED VIRTUALITY STRATEGY FOR TRAINING CONSTRUCTION HAZARD RECOGNITION

机译:节省:增加培训施工危险识别的增强虚拟性战略

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Although the ratio of injuries and fatalities have decreased since 2009, yet their high rates still makes construction to be one of the top hazardous industries. OSHA and many companies ~ that treat safety as their core value - have stated repeatedly their strong desire for novel methods that speed up safety improvements. Improving the capability of hazard recognition in construction work is frequently identified as the first step towards building all other safety procedures. Without sufficient awareness and proper training on hazard recognition, even the best safety programs would not touch their desired expectations. Also, there is a need to better understand how different safety programs can maximize hazard recognition skills in workers. In response to these urgent needs, this paper explored a new training strategy for improving hazard recognition capability of construction workers. An augmented virtuality (AV) environment named SAVES (System for Augmented Virtuality Environment Safety) was developed and tested. SAVES integrates BIM with photographs of typical energy sources on a jobsite, allowing trainees to navigate and explore the built-in augmented training scenarios. This paper presents the model development and the implementation results, to validate how such AV method can be designed to improve the capability of hazard recognition. The modeling process, analysis and the lessons learned are discussed in detail.
机译:虽然自2009年以来伤害和死亡率的比例降低,但他们的高利率仍然使建设成为顶级危险产业之一。 OSHA和许多公司〜将安全性视为核心价值 - 已经反复陈述他们对加速安全改进的新方法的强烈愿望。提高施工工作中的危险识别能力经常被确定为建立所有其他安全程序的第一步。没有足够的意识和适当的危险认可培训,即使是最好的安全计划也不会触及所需的期望。此外,需要更好地了解不同的安全计划如何最大化工人的危险识别技能。为应对这些迫切需求,本文探讨了提高建筑工人灾害识别能力的新培训策略。开发并测试了名为Saves(增强虚拟性环境安全系统的系统)的增强虚拟(AV)环境。 Saves将BIM集成在乔科特上的典型能源照片,允许受训人员导航和探索内置的增强培训方案。本文介绍了模型开发和实现结果,以验证这种AV方法如何旨在提高危险识别能力。详细讨论了建模过程,分析和学习的经验教训。

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