Collaborative Research: Measuring, Predicting, and Improving Construction Safety by Improving Hazard Signal Detection with Augmented Virtual Environments

协作研究：通过增强虚拟环境改进危险信号检测来测量、预测和提高施工安全

• 批准号: 1362263
• 负责人: Matthew Hallowell
• 金额: $ 20.96万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1362263&HistoricalAwards=false
• 关键词: Collaborative; Research; Measuring; Predicting; Improving

Each year the construction industry has a disproportionate injury rate compared to other industries. One of the main causes of these injuries is human error, which could be exacerbated by skill deficiency associated with signal detection (defined as the ability of an individual to detect visual stimuli that indicate the existence of a safety hazard). From psychological and engineering standpoints little is known about signal detection or if this skill can be accurately measured, predicted, or improved with emerging technologies. This award supports interdisciplinary research that investigates if signal detection can be reliably improved through exposure to and feedback from an augmented virtual construction environment. Additionally, the research explores if an individual's signal detection skill in a virtual environment can predict their skill in real construction environments. As new knowledge is gained, it will be shared with industry practitioners through presentations to national communities of practice, integrated into multiple levels of civil engineering and psychology curricula, and embedded in the final version of the virtual system that will disseminated broadly for education and safety training.The main objective of this research is to test hypotheses that augmented virtual environments can be used to improve signal detection skill by over 30% and that over half of the variability in signal detection skill in field environments can be predicted by one's performance in an augmented virtual system. These hypotheses will be tested using a multiple baseline design that has been adapted from pharmaceutical research to address the dynamic nature of the construction industry. The experimental results will be statistically verified using interrupted time-series regression models. If successful, the adaptation of multiple baseline design and statistical verification would serve as a new approach of experimental research for construction. Additionally, a strong relationship between performance in virtual and field environments would indicate that high-fidelity augmented virtual environments provide a risk-free proxy for measuring signal detection skill, potentially transforming the way that construction safety is studied.

每年，建筑业与其他行业相比都有不成比例的伤害率。这些伤害的主要原因之一是人为错误，而与信号检测（定义为个人检测指示存在安全危险的视觉刺激的能力）相关的技能缺陷可能会加剧人为错误。从心理学和工程学的角度来看，人们对信号检测知之甚少，也不知道这种技能是否可以通过新兴技术准确测量、预测或改进。该奖项支持跨学科研究，研究信号检测是否可以通过接触增强的虚拟施工环境和反馈来可靠地改进。此外，该研究还探讨了一个人在虚拟环境中的信号检测技能是否可以预测他们在真实的建筑环境中的技能。随着新知识的获得，将通过向国家实践社区的介绍与行业从业人员分享，并将其纳入多层次的土木工程和心理学课程，本研究的主要目的是验证增强虚拟环境可用于改善信号检测的假设在现场环境中信号检测技能中超过一半的可变性可以通过一个人在增强的虚拟系统中的表现来预测。这些假设将使用多基线设计进行测试，该设计已从制药研究中改编，以解决建筑行业的动态性质。实验结果将使用中断时间序列回归模型进行统计验证。如果成功的话，多基线设计和统计验证的适应性将成为建筑实验研究的新途径。此外，在虚拟和现场环境中的性能之间的强有力的关系将表明，高保真增强虚拟环境提供了一个无风险的代理测量信号检测技能，潜在地改变了建筑安全研究的方式。