This project aims to create an automated bridge inspection framework that leverages drone technology and state-of-the-art machine learning techniques for the precise and efficient detection of various defects, including cracks and other structural issues, on bridge decks. This framework will not only streamline the inspection process, but also provide infrastructure managers with a detailed understanding of the bridge's condition, thus promoting better decision-making regarding maintenance and repair activities. By adopting this advanced framework, transportation authorities can improve the safety, reliability, and longevity of their infrastructure assets while minimizing costs and disruptions associated with traditional inspection methods.

Ground penetrating radars (GPR) are used for subsurface exploration purposes, and for locating underground utilities. We have GSSI and MALA GPR system with multiple frequencies that are readily avaiable for data collection. One of our ongoing projects involves designing and constructing a controlled earth fill embedded with known objects (e.g., pipes and voids) at pre-determined depths. This setup serves as a research test bed. The test bed, located at the KSU Field Station, extends to a length of 60 feet and a depth of approximately 5 feet. 

The swift reconnaissance of building damage is pivotal in disaster response and recovery operations. Drones have proven instrumental in this respect, being deployed to capture aerial images of disaster-stricken areas and assess the extent of building damage. This project seeks to automate the process of building damage reconnaissance, thereby optimizing drone mission planning for disaster response. By leveraging cutting-edge technology, we aim to enhance the efficiency and effectiveness of disaster management.