دانلود رایگان مقاله انگلیسی تحلیل تصویر برای توزیع شکاف و برآورد عرضی برای سازه های بتن مسلح - الزویر 2018

ABSTRACT

Crack observation is important for evaluating the structural performance and safety of reinforced concrete (RC) structures. Most of the existing image-based crack detection methods are based on edge detection algorithms, which detect cracks that are wide enough to present dark areas in the obtained images. Cracks initiate as thin cracks, generally having width less than the width of a pixel in images; such cracks are generally undetectable by edge detection-based methods. An image analysis method is proposed to observe the development and distribution of thin cracks on RC surfaces; it also allows estimation of crack widths. Image matching based on optical flow and subpixel is employed to analyze slight concrete surface displacements. Camera calibration is included to eliminate perspective effects and lens distortion. Geometric transformation is adopted so that cameras do not need to be perpendicular to the observed surface or specified positions. Formulas are proposed to estimate the width of shear crack opening. The proposed method was then applied to a cyclic test of an RC structure. The crack widths and their development analyzed by the image analysis were verified with human inspection in the test. In addition, concrete surface cracks that appeared at a very early stage of the test could be observed by the proposed method before they could be detected by the naked eye. The results thus demonstrate that the proposed image analysis method offers an efficient way applicable not only for structural tests but also for crack-based structural-healthmonitoring applications.

5. Summary

This study proposed an image analysis method to estimate crack opening development in a reinforced concrete (RC) tubular structure subjected to a cyclic displacement at its top. The image analysis results of one of the stereo imaging systems in the experiment (i.e., the north one) were presented in this paper. The crack opening fields were estimated by analyzing the displacement fields using the crack analysis method proposed in this paper.

Cracks as thin as 0.02–0.03 mm were difficult to detect to the naked  eye, but could be clearly observed by image analysis. Crack opening width comparison between manual pen marks and image analysis showed that the differences were generally less than or equal to 0.03 mm. While manually marked cracks were available at only a few pause steps (seven steps in this study), image analysis provided dozens of steps of a continuous crack distribution and generated a video of crack changes. It also provided sufficient information to obtain crack opening width data of any part of any selected crack at a selected experimental time point.

This method is only applicable when cameras are firmly fixed and observed region is pre-painted with random patterns. To address these limitations, in follow-up studies, image analysis algorithms and methods can be further studied. Future algorithms can be developed to estimate camera movement by tracking predefined unmoving points or the background. Edge-detection- or machine-learning-based methods can be incorporated with the proposed method in cases where prepainted patterns on the observed surfaces are insufficient.