The digital image correlation method is proposed by Peter et al

The digital image correlation method is proposed by Peter et al. [3] in 1982. Then, many scholars applied and inferred mechanics and mathematics theories Enzastaurin for expanding the application of DIC method [4�C10]. The research team of digital image correlation method, Taiwan, has developed two-dimensional DIC method for observing tiny crack phenomenon cracks in brick walls, warp cracks developed in reinforced concrete, cracks developed in brittle material, and warp cracks developed in light aggregate concrete. It was also applied to observe microscopically metal anisotropic behavior, testing steel plate damages mechanically. Then, this research team developed dynamic DIC method to monitor bridge deformation under traffic loads and the structural dynamic response of reducedscale five-story building under excitation of earthquake forces [10].

These test results achieve quite well-experimental results, compared with the traditional test method. Thus, this research develops this real-time monitoring techniques based on establishing the correlation coefficients of digital images, for using continuous parameter with Inter-Story Drift Mode Shape, IDMS, to analyze cantilever beam with various damage conditions and locations.2. Methodology2.1. Static Digital Image Correlation MethodThe principle of digital image correlation method, DIC, is based on the ��Finding Algorithm�� that developed the technology of DIC for comparing the local correlation of two images before and after deformation. Therefore, the main concept of digital image correlation method is based on the finite element method.

The images are divided into small mesh. The mesh of original image can be compared with those in the image after transformation so that the corresponding location of this selected zone in the deformed image can be identified. Thus, ��structural speckle�� will be AV-951 established on the specimen surface of cantilever beam, shown in Figure 1. This makes a different grayscale distribution in the image. This grayscale distribution characteristic can be utilized to identify the corresponding position of images before and after displacement. The relationship between deformed and undeformed images can be identified. The central point prior to deformation is point P; it is changed to point P* after deformation, shown in Figure 2. The functional relationship is expressed asx?=x+u0+?u?xdx+?u?ydy,(1)y?=y+v0+?v?ydx+?v?ydy.

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