After a disaster, such as an earthquake, inspectors are tasked with assessing the integrity of affected buildings and structures. Depending on the scale of the disaster, the number of required inspections can range into the thousands. There are both public safety and economic pressures to consider, and so rapid and accurate assessments of buildings and structures are vital. The goal of this research project is to develop a method using digital image analysis and artificial intelligence to assess the integrity of civil structures after a natural disaster. Fully realized, this technology will enable post-disaster inspectors to rapidly and accurately estimate structural damage using only a digital camera and portable computer.
Research in a comprehensive method of computer vision-based structural assessment will be pursued, one that is flexible enough to operate in highly varied and challenging field environments. To be truly comprehensive, such a methodology must be hierarchical, first recognizing the system-level context of structural components observed in an image and then leveraging that information to augment localized descriptions of damage extracted from segmentation routines. In this research program, the visual and instrumentation records of NEES experiments, available through the NEESHub, will be mined and analyzed to validate the algorithm. The research program will advance understanding as to how visually observable damage correlates to structural performance, and will provide insights into the suitability of hierarchical learning techniques for use in the field of computer vision-based structural assessment. More broadly, the project will result in an extensible method of visual, non-contact structural assessment that will provide a foundation for future structural monitoring system.
