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Post-Earthquake Assessment of Bridge Columns Using AI Enabled Computer Vision
April 7, 2022 @ 1:00 pm - 2:00 pm
Presentation
Post-Earthquake Assessment of Bridge Columns Using AI Enabled Computer Vision
Modern seismic design codes ensure a large displacement capacity and prevent total collapse for bridges. However, this performance objective is usually attained at the cost of damage to target ductile members. For reinforced concrete (RC) bridges, the columns are usually the main source of ductility during an earthquake in which concrete cover, core, and reinforcement may damage, and the column may experience a large permanent lateral deformation. The main goal of this project was to accelerate post-earthquake RC bridge column inspection using “computer vision”. Instead of sending trained personnel to the affect sites, local personnel equipped with an assessment software built-in a drone, as a mobile application, or as a cloud-based tool can be deployed to evaluate the bridge condition. An artificial intelligence (AI) enabled software was developed based on a photograph database of RC bridge columns to quickly detect cracking, spalling, and reinforcement, to comment on the RC column damage state, and to tag (green, yellow, or red) the column/bridge based on the extent of the damage. The software performs both preliminary damage assessment (PDA) and detailed damage assessment (DDA) using a few column parameters and provides a reasonable accuracy with a safe assessment. The presentation highlights the key finding of the study.
Speaker
Mostafa Tazarv, PhD, PE Associate Professor
South Dakota State University
Dr. Mostafa Tazarv, PE, is an associate professor in the department of Civil and Environmental Engineering at South Dakota State University (SDSU). Prior to joining SDSU in 2015, Dr. Tazarv was a post-doctoral scholar at the University of Nevada, Reno, where he also received his Ph.D. His research interests include the behavior of concrete structures, advanced materials for civil infrastructure, accelerated bridge construction, large-scale testing.