Structural engineering focuses on improving how we design and maintain infrastructure systems. The research spans the fields of civil engineering, mechanical engineering, and computer science.
Our research interests include:
Virtual Robotic Bridge Inspection
The research team studies how to convert robotic inspection information into virtual computer worlds that can be explored by a human inspector. As robots become a more accepted as infrastructure inspection tools, we must consider how a human will interact with the information that robots capture. This is fundamentally a data representation problem. To solve this problem, we use techniques adapted from computer vision and virtual reality equipment. We hope this research makes the hazardous job of bridge inspection safer for a human. Principal investigator: David Lattanzi.
Structural Evaluation through Computer Vision
After a disaster, such as an earthquake, inspectors must assess the integrity of all affected structures. Depending on the scale of the disaster, the number of required inspections can range into the thousands, so rapid assessments are vital. This project aims to develop a method of assessing the integrity of civil structures by combining digital image analysis and artificial intelligence. Fully realized, the proposed technology will enable post-disaster inspectors to rapidly and accurately estimate structural damage using only a digital camera and portable computer. Principal investigator: David Lattanzi.
Response of Structures Subjected to Explosions
The team investigating the response of structures that are subjected to short-duration dynamic loads, such as air-blast, impact, fragmentation, and underwater explosion. Research activities include the use of fiber-reinforced polymer (FRP) composites for mitigating blast effects on buildings, penetration mechanics of projectiles on concrete targets, developing predictive capabilities for characterizing failure mechanisms in reinforced concrete slabs, and modeling the response of submerged structures for underwater explosion. Principal investigator: Girum Urgessa.
Extracting Finite Element Models from Images
The team studies ways of automatically extracting finite element models from collections of images. Using a combination of structure from motion (SfM) algorithms and 3D computer vision, we are working on ways to automatically recognize and assemble structural components, followed by FE model generation and updating based on damage detected in the image collections. The proposed methodology has applications beyond post-disaster condition assessment, from routine inspection to infrastructure management. Principal investigator: David Lattanzi.