Speaker: Mubarak F. Abu Zouriq
When: Friday, April 18th, 1:00PM
Where: KH A549 (Lincoln)
Zoom: https://unl.zoom.us/j/99533249179
Title: Smart Bridge Health Assessment through Deep Learning Structural Damage Detection Leveraging Virtual Strains
Abstract: Bridges are increasingly vulnerable to structural deficiencies from aging materials, increasing loads, and extreme events, possibly leading to catastrophic failures if not appropriately monitored. Structural health monitoring (SHM) was developed to interrogate bridge conditions to ensure safety and functionality by detecting early signs of deterioration through structural response monitoring (e.g., strains, vibrations) using strategically mounted sensors. However, challenges associated with monitoring large structures using a finite number of sensors, including installation costs and placing sensors difficulties at all key locations, often result in limitations that hinder efficient SHM. Research summarized herein attempted to address these limitations by developing a more robust monitoring framework that overcomes spatial and operational limitations associated with conventional SHM methods. A novel deep-learning framework was proposed to enhance response estimation capabilities at non instrumented locations and the likelihood of detecting early-stage damage.