Computer vision methods have received success in damage inspection of different types of structures such as reinforced concrete, steel, and masonry. However, vision-based research on reinforced masonry structures remains rather limited. In this project, we are developing a multi-sensor setup using 3D computer vision and 3D point cloud processing techniques to detect and quantify cracks, spalling, and reinforcement exposure of the reinforced masonry structures. In addition, we are fusing the sensor data to measure structural deformation measurement in 3D space.
First, prism tests have been carried out.
Reinforced masonry prism specimens
Further, reinforced masonry wall tests will be conducted.
Test setup
During and after the test, the vision and 3D point cloud processing algorithms will be implemented to detect structural damages and quantify structural deformations.
This is an active ongoing project. Please feel free to revisit this page at any time later.
Team composition: Xiao Pan, Elmira Faraji, and T.Y. Yang