Post-Earthquake Aerial Reconnaissance of Geotechnical Engineering Systems

Regents Of The University Of Michigan - Ann Arbor, Ann Arbor MI

Investigators

Abstract

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.