Event Title

Inverse Bridge Health Monitoring Method

Presenter Information

Daniel Peterson, Minnesota State University - Mankato
Garrick Murphy, Minnesota State University - Mankato

Location

CSU 255

Start Date

20-4-2015 2:10 PM

End Date

20-4-2015 3:10 PM

Student's Major

Mechanical and Civil Engineering

Student's College

Science, Engineering and Technology

Mentor's Name

Saeed Moaveni

Mentor's Email Address

saeed.moaveni@mnsu.edu

Mentor's Department

Mechanical and Civil Engineering

Mentor's College

Science, Engineering and Technology

Description

The current practice of bridge health monitoring is based primarily on visual inspections. The major concern with this technique is the lack of accessibility to critical bridge components. The motivation behind this research is to find an easier way to monitor the health of a bridge in order to prevent disasters such as the I-35 bridge collapse from happening again. The main purpose of this study was to test an inverse model that was developed by Dr. Moaveni to determine the variation in the area moment of inertia (due to corrosion or cracks) of a bridge from a static load and deflection data. The changes in the moment of inertia of a bridge can then be used to predict where cracks and/or corrosion may exist. For a given traffic load, the deflection of a bridge would increase if the bridge had structural components with corrosion and cracks. To test the validity of the inverse model, numerical experiments were performed using different loading and deflection data for a bridge with and without cracks or corrosions. The test results suggest that variation in area moment of inertia of a bridge can be detected with reasonable accuracy of less than 10%. In the follow-up study, the inverse model will be modified to use dynamic loading (by monitoring the actual traffic load) and the resulting deflections to determine the changes in the area moment of inertia of the bridge.