Electronic Thesis and Dissertation Repository

Thesis Format

Integrated Article

Degree

Master of Engineering Science

Program

Civil and Environmental Engineering

Supervisor

Sadhu, Ayan

Abstract

During the initial construction period, “early-age” masonry walls are susceptible to lateral loads induced by wind or earthquake, which may result in damages or catastrophic failures. To mitigate such consequences at construction sites, temporary bracings are adopted to provide lateral support to masonry walls until they are matured enough to serve as the inherent lateral system of the structure. However, current temporary bracing guidelines provide oversimplified design due to the lack of available information on the material properties of early-age masonry. Moreover, there are no existing techniques for monitoring masonry walls to detect cracks due to construction activities. This thesis presents innovative techniques for the structural health monitoring of early-age masonry structures at construction sites. The stress-strain behavior of early-age masonry structures that have been cured for 3 to 72 hours was estimated through a detailed uniaxial tensile testing program. A 3D microscopic numerical model with cohesion-based interaction surfaces was developed to accurately estimate the tensile behavior and failure patterns of early-age masonry assemblages. A novel hybrid image processing and deep learning algorithm are then proposed for the efficient crack detection in masonry structures at the construction site. Finally, a general discussion on the results, contributions, and future research are provided.

Summary for Lay Audience

Early-age masonry structures are weak in the lateral direction and are susceptible to damages due to extreme wind events and earthquakes. To mitigate the potential of these damages, temporary braces are installed to provide structural resistance against these natural phenomena. However, the design of these braces is based on the strength properties of the masonry structures. During the initial construction period, there is minimal information available on these properties, and therefore the bracing design becomes inaccurate. Additionally, there are no existing autonomous techniques to detect cracks of masonry structures under construction. In this thesis, the detection and prevention of damages in early-age masonry structures form the key objectives. Numerous experiments were conducted to determine the tensile strength of masonry prisms during initial construction. A numerical model was developed to accurately depict the strength and failure behavior of masonry so that the strength can be estimated during construction. Lastly, an automatic crack detection algorithm was created using a hybrid Artificial Intelligence technique to allow for the rapid detection of damages.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Available for download on Tuesday, August 24, 2021

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