Faculty
Faculty of Engineering
Supervisor Name
Ayan Sadhu
Keywords
Structural Health Monitoring, Augmented Reality, Artificial Intelligence, Damage Classification, Multiclass Identification, Damage Quantification
Description
Structural Health Monitoring (SHM) is the assessment of bridges and observation of data regarding these bridges over time to monitor their evolution and detect the presence of any possible damages. However, existing methods to perform structural inspections in bridges are high in cost, time-consuming and risky. Inspectors use expensive equipment to reach a certain area of the bridge to inspect it, and at different heights, this can pose a risk to the inspector’s safety. This study aims to find cheaper, faster, and safer ways to perform structural inspections using augmented reality and artificial intelligence. The developed system uses a machine learning model to detect and classify four different types of damage, the system also provides length, area, and perimeter measurements to further assess the severity of the damage.
Acknowledgements
This project is part of the Undergraduate Summer Research Internship Program (USRI) at Western University.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Document Type
Poster
Included in
Artificial Intelligence and Robotics Commons, Civil Engineering Commons, Software Engineering Commons, Structural Engineering Commons
Damage Assessment in Aging Structures using Augmented Reality
Structural Health Monitoring (SHM) is the assessment of bridges and observation of data regarding these bridges over time to monitor their evolution and detect the presence of any possible damages. However, existing methods to perform structural inspections in bridges are high in cost, time-consuming and risky. Inspectors use expensive equipment to reach a certain area of the bridge to inspect it, and at different heights, this can pose a risk to the inspector’s safety. This study aims to find cheaper, faster, and safer ways to perform structural inspections using augmented reality and artificial intelligence. The developed system uses a machine learning model to detect and classify four different types of damage, the system also provides length, area, and perimeter measurements to further assess the severity of the damage.