Post-Earthquake Aerial Reconnaissance of Geotechnical Engineering Systems

岩土工程系统震后空中勘察

• 批准号: 1362975
• 负责人: Dimitrios Zekkos
• 金额: $ 38.98万
• 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: 2014-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1362975&HistoricalAwards=false
• 关键词: Post; Earthquake; Aerial; Reconnaissance; Geotechnical

The Tohoku (2011), Christchurch (2011), and Canterbury (2010) Earthquakes affected two nations known for their pioneering advances in earthquake engineering; yet both nations experienced extreme levels of destruction. These events are poignant reminders that many lessons are yet to be learned to ensure we can engineer truly resilient communities. Post-earthquake reconnaissance missions are absolutely vital to the experience-based learning process required to advance our understanding of natural hazards and their impact on geotechnical systems. While post-earthquake reconnaissance has provided a wealth of learning experiences, current practices suffer from drawbacks due to inefficiencies in manual collection of large perishable datasets, high costs associated with deployment of teams, accessibility limitations, and safety considerations. Unmanned Autonomous Aerial Vehicles (UAAVs), using the latest technological and computational tools available, will enable engineers to collect higher quality, more objective, and more extensive perishable datasets on the performance of geotechnical systems during reconnaissance missions. This project paves the way for the use of UAAV technology for extreme-event reconnaissance of geotechnical systems. The societal benefits associated with UAAV-based post-event reconnaissance are significant and include more effective learning from disasters abroad before they hit our nation, more efficient post-event responses leading to more resilient civil infrastructure systems, and quicker economic recovery of affected regions. A transformative framework for post-event reconnaissance and decision making is planned based on the use of highly-mobile and sensor-rich UAAVs. Adoption of UAAV technology represents a paradigm shift in post-event reconnaissance because it allows geotechnical engineers to more efficiently collect large, higher quality, and more objective reconnaissance data. The platform will have the ability to also process large collected datasets to (a) automatically detect and map damage features at a regional scale; and (b) generate more accurate 3D maps of surface deformations and geometry using LIDAR-enhanced imagery. In addition, the UAAV's ability to communicate with field-deployed wireless sensors and collect additional site characterization data will be investigated. Towards this end, the project aims to prove that the UAAV and a network of wireless geophones can execute surface wave measurements to estimate shear wave velocity using weights dropped by the UAAV and characterize conditions to greater depths than currently feasible by reconnaissance teams. The UAAV communication platform will also efficiently disseminate geo-referenced data to a community of experts who can analyze the data and provide expert advice to ground-based reconnaissance teams.

Tohoku（2011），Christchurch（2011）和坎特伯雷（Canterbury）（2010）地震影响了两个国家，以其地震工程的开拓性进步而闻名。然而，两国都经历了极端的破坏。这些事件令人难以置信地提醒人们，尚未学会许多教训，以确保我们可以设计真正具有韧性的社区。侦察后侦察任务对于提高我们对自然危害及其对岩土系统的影响所需的基于经验的学习过程至关重要。虽然地球后的侦察提供了丰富的学习经验，但由于手动收集大型易腐数据集的效率低下，当前的做法遭受了弊端，与部署团队部署，可及性限制和安全考虑有关的高成本。使用最新的技术和计算工具可用的无人自动驾驶汽车（UAAVS）将使工程师能够在侦察任务期间收集有关岩土技术系统性能的更高质量，更客观且更广泛的易腐数据集。 该项目为使用UAAV技术用于极端事件的岩土技术系统铺平了道路。与基于UAAV的事后侦察相关的社会利益很重要，包括在袭击我们的国家之前向国外灾难进行更有效的学习，更有效的事后响应，导致更具弹性的民用基础设施系统以及受影响地区的更快的经济复苏。 根据使用高度摩托和传感器丰富的UAAVS的使用，计划了事后侦察和决策的变革性框架。 UAAV技术的采用代表了事后侦察的范式转变，因为它允许岩土工程师更有效地收集大型，更高质量和更客观的侦察数据。该平台将有能力处理大型收集的数据集，以（a）自动检测和映射区域尺度的损害特征； （b）使用LIDAR增强图像生成更准确的表面变形和几何形状的3D图。此外，将研究UAAV与现场部署无线传感器进行通信并收集其他站点表征数据的能力。 为此，该项目的目的是证明UAAV和无线地球器网络可以执行表面波测量，以使用UAAV掉落的权重估算剪切波速度，并将条件表现到比侦察队当前可行的更深度的条件。 UAAV通信平台还将有效地向一个可以分析数据并向基于地面的侦察团队提供专家建议的专家社区有效传播地理参考的数据。