Damage Detection of Concrete and Masonry Structures

Overview

Structural concrete and masonry materials are widely used in building construction. The damage types of reinforced or unreinforced concrete and masonry structures are very similar, such as cracks, spalling, crushing, exposure, buckling, yielding of steel reinforcement, etc. This project aims to develop and apply novel deep learning, computer vision, and 3D point cloud techniques for automatic classification, localization, and quantification of these damage features.

Damage Detection Flowchart

References

[1]. Tavasoli, S., Pan, X., & Yang, T. Y. (2023). Real-time autonomous indoor navigation and vision-based damage assessment of reinforced concrete structures using low-cost nano aerial vehicles. Journal of Building Engineering, 68, 106193.

[2]. Faraji Zonouz, E., Pan, X., Hsu, Y., Yang T.Y. (2023). 3D vision-based structural masonry damage detection. Canadian Conference - Pacific Conference on Earthquake Engineering (CCEE-PCEE) 2023, Vancouver, British Columbia, Canada.

[3]. Tavasoli, S., Pan, X., Yang, T. Y., Gazi, S., & Azimi, M. (2023). Autonomous damage assessment of structural columns using low-cost micro aerial vehicles and multi-view computer vision. Canadian Conference - Pacific Conference on Earthquake Engineering (CCEE-PCEE) 2023, Vancouver, British Columbia, Canada.

[4]. Pan, X., & Yang, T. Y. (2020). Postdisaster image-based damage detection and repair cost estimation of reinforced concrete buildings using dual convolutional neural networks. Computer-Aided Civil and Infrastructure Engineering, 35(5), 495-510.