Dihao Ai

By Dihao Ai

Accurately detecting and tracking cracks on construction materials, such as concrete and rock surfaces, is of paramount importance in the field of structural engineering and materials science. The presence and propagation of cracks in these materials can have significant implications for structural integrity and safety, making it crucial to develop robust methods for crack detection and tracking. This work explores the application of computer vision and deep learning algorithms in the visual recognition and prediction of crack propagation trajectories in construction materials. Specifically, a Split Hopkinson Pressure Bar system and a static compression loading platform were constructed to simulate fracture processes in concrete and rock materials. The high-speed camera was used to capture the fracture process as a video or image sequence. An automatic pixel-level crack segmentation method was proposed to extract cracks at critical moments within the fracture video. Subsequently, the Long Short-Term Memory network was employed to learn temporal patterns and dependencies within the crack segmentation masks across consecutive frames. By leveraging the learned temporal patterns, the network can estimate how cracks will evolve and propagate in subsequent frames. The proposed method has potential applications in assessing structural integrity, understanding fracture mechanisms, and informing maintenance and repair decisions.

Part of the book: Digital Image Processing - Latest Advances and Applications [Working title]