Innovations Series

A Cost-and Time-Efficient Transfer Learning Framework for Vibration-Based Damage Detection in Bridges

Discover a groundbreaking approach to bridge damage detection that combines the efficiency of reduced-order 3D numerical models with the precision of detailed 3D modeling. This innovative transfer learning framework leverages deep learning to drastically cut computational time and costs while preserving accuracy, offering a practical and cost-and time-effective solution for vibration-based structural health monitoring in bridges.

1 hour CE credit

Registration

This is a virtual event on Zoom. All registrants MUST have a free Zoom account to access the webinar.

Members - FREE
Nonmembers - $25

About the Speaker

Pariya Aghelizadeh Mofrad
PhD Student
University of Illinois at Chicago
Pariya is a PhD student at the University of Illinois at Chicago (UIC), specializing in vibration-based damage detection methods for bridges using deep learning. She values hands-on experience, which has been central to her career, particularly during her last internship at Wiss, Janney, Elstner Associates (WJE). There, she got involved in inspections, load testing, rating, and analysis of various bridges, including Bascule Bridges. She is passionate about solving real-world challenges in transportation engineering.”