Department of Construction Management, Tsinghua University, China.
School of Built Environment, Curtin University, Australia.
Int J Occup Saf Ergon. 2020 Dec;26(4):740-752. doi: 10.1080/10803548.2018.1486528. Epub 2018 Oct 8.
Navigated inspection seeks to improve hazard identification (HI) accuracy. With a tight inspection schedule, HI also requires efficiency. However, lacking quantification of HI efficiency, navigated inspection strategies cannot be comprehensively assessed. This work aims to determine inspection efficiency in navigated safety inspection, controlling for HI accuracy. Based on a cognitive method of the random search model (RSM), an experiment was conducted to observe the HI efficiency in navigation, for a variety of visual clutter (VC) scenarios, while using eye-tracking devices to record the search process and analyze the search performance. The results show that the RSM is an appropriate instrument, and VC serves as a hazard classifier for navigation inspection in improving inspection efficiency. This suggests a new and effective solution for addressing the low accuracy and efficiency of manual inspection through navigated inspection involving VC and the RSM. It also provides insights into the inspectors' safety inspection ability.
导航检查旨在提高危害识别 (HI) 的准确性。由于检查计划紧张，HI 还需要提高效率。但是，由于缺乏 HI 效率的量化，因此无法全面评估导航检查策略。本工作旨在确定导航安全检查中的检查效率，同时控制 HI 的准确性。基于随机搜索模型 (RSM) 的认知方法，进行了一项实验，以观察在各种视觉杂乱 (VC) 情况下导航时的 HI 效率，同时使用眼动跟踪设备记录搜索过程并分析搜索性能。结果表明，RSM 是一种合适的工具，VC 可作为用于提高检查效率的导航检查的危害分类器。这为通过涉及 VC 和 RSM 的导航检查来解决手动检查准确性和效率低的问题提供了一种新的有效解决方案。它还深入了解了检验员的安全检查能力。
