Collaborative Research: Project Smart-Recon: Smart Device-Enabled Reconnaissance after Earthquakes

合作研究：Smart-Recon 项目：地震后智能设备侦察

• 批准号: 1362547
• 负责人: Sherif El-Tawil
• 金额: $ 22.09万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 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: 2014-06-01 至 2019-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1362547&HistoricalAwards=false
• 关键词: Collaborative; Research; Project; Smart; Recon

Whenever a catastrophic event occurs, reconnaissance teams are deployed within the affected areas to conduct visual inspections of buildings. The teams tag the buildings red, yellow or green to indicate their probable condition and permitted use. This function often takes weeks. The central premise of this work is that widespread citizen ownership of smartphones and devices can be leveraged to automate and significantly accelerate this first reconnaissance effort. Under this project, research will be conducted on how to integrate measurements performed by sensors that are typically part of most modern smart devices (e.g. accelerometers, gyroscopes, etc.) to infer information about their motion during a seismic event. By increasing the speed and accuracy with which building damage may be assessed in the aftermath of natural disasters, the proposed reconnaissance technology will reduce potential hazards and hardships to citizens and will provide enormous cost savings. Knowing this information will also enable first responders to optimize their response and physical inspection teams to prioritize their efforts, thereby minimizing confusion in the aftermath of a disaster. On the educational front, this project will have a substantial impact on the development of human resources. By bridging civil and electrical engineering, the students who will work on this project will attain a multi-disciplinary education at the intersection of both disciplines.To attain the project's objectives, new algorithms will be developed to permit smart devices to sense (or learn) the type of surface they are on and use that knowledge to infer information about their motion during a seismic event. Since the motion of each device may be contaminated by secondary motion, e.g. sliding on a surface, signal processing techniques will be employed to investigate how ensemble observations across multiple sensors that experience correlated motion can be used to yield highly accurate estimates of floor motion. Studies will also be conducted to explore the necessary level of accuracy required for device location within a building and device-measured parameters to ensure a meaningful assessment of seismic structural demands. The automated first reconnaissance effort will be enabled through computation of interstory drift ratios and comparing those ratios to known damage limits. As such, it is possible to electronically tag buildings for their level of damage within minutes of a seismic event.

每当发生灾难性事件时，侦察队就会被部署到受灾地区，对建筑物进行目视检查。参赛队将建筑物标记为红色、黄色或绿色，以表明它们的可能状况和允许使用。这个过程通常需要数周时间。这项工作的中心前提是，智能手机和设备的广泛公民所有权可以用来自动化并显着加速第一次侦察工作。在这个项目下，将研究如何整合传感器进行的测量，这些传感器通常是大多数现代智能设备（例如加速度计，陀螺仪等）的一部分，以推断地震事件期间它们的运动信息。通过提高在自然灾害发生后评估建筑物损坏的速度和准确性，拟议中的侦察技术将减少对公民的潜在危险和困难，并将节省大量成本。了解这些信息还将使第一响应者能够优化他们的反应，并使物理检查小组能够优先考虑他们的努力，从而最大限度地减少灾难后的混乱。在教育方面，这个项目将对人力资源的发展产生重大影响。通过连接土木和电气工程，参与这个项目的学生将在这两个学科的交叉点获得多学科的教育。为了实现该项目的目标，将开发新的算法，使智能设备能够感知（或学习）它们所处的表面类型，并利用这些知识来推断地震事件期间它们的运动信息。由于每个设备的运动可能受到二次运动的污染，例如在表面上滑动，因此将采用信号处理技术来研究如何使用经历相关运动的多个传感器的集合观测来产生高度准确的地板运动估计。还将进行研究，以探索建筑物内设备位置和设备测量参数所需的必要精度水平，以确保对地震结构需求进行有意义的评估。通过计算层间漂移比，并将这些比与已知损伤极限进行比较，将实现自动的首次侦察工作。因此，在地震发生后的几分钟内就可以对建筑物的损坏程度进行电子标记。