| Summary: | 碩士 === 國立臺灣大學 === 土木工程學研究所 === 107 === Structural health monitoring becomes more and more important in practice because this technology can elongate the structural life cycle as well as protect structures against natural hazards. Moreover, structural health monitoring systems can automatically inform residents and users for the current condition of structures and engineers for the current performance. In past, structural health monitoring relies on the contact sensors to acquire structural responses and then diagnoses structures in accordance with the measurements. In this research, a new method is developed to detect and quantify the concrete cracks based on the noncontact image measurements. This method integrates computer vision and deep learning to identify the crack existence and geometry. The identified cracks can provide indirect information for experts to further investigate the structural conditions.
This study exploits deep learning and transfer learning, e.g., the tools in the category of artificial intelligence, to train and establish a concrete segmentation model that can identify the locations of cracks in images. In this model, the crack features can be obtained from the convolutional neural network and then automatically identify whether the cracks are present and where the cracks are. Then, the image processing and computer vision are implemented to highlight and extract these cracks from images. Finally, the geometry of these cracks (i.e., lengths and widths) can be calculated by image measurement techniques.
To verify the proposed method, this study employs the images of concrete surface cracks obtained from the real-world structures and then evaluate the reliability of this method. In the verification, the pre-calibrated stereo camera model with a two-camera setup is used to verify the actual lengths and widths of cracks. The calculated results are compared with the actual measurements. As a result, the proposed method can successfully determine crack geometry. Moreover, the method also benefits users to obtain crack information and to turn into performance evaluation of concrete structures for structural health monitoring.
|
|---|
