A study on crack detection using Image processing

The crack of concrete structure plays an important role in the durability and safety of structure. Therefore, the features such as width, length, and direction of that must be measured periodically. The conventional method of measurement of cracks is manually sketched, however. it takes a fairly long time and lacks quantitative objectivity. This study proposes the algorithm to extract and analyze cracks automatically. The proposed algorithm is composed of two sub-algorithms. The extraction algorithm includes elimination of effect due to light, binarization. and noise reduction. The analysis algorithm includes thinning process, labeling, and calculation of crack width, length, and direction. The test to demonstrate the algorithm is fulfilled using the images of cracks on real concrete surface

Development of an algorithm for crack pattern recognition

This study proposes an algorithm for recognition of crack patterns, which includes horizontal, vertical, diagonal$(-45^{\circ})$, diagonal$(+45^{\circ})$, and random cracks, based on image processing technique and artificial neural network. A MATLAB code was developed for the proposed algorithm, and then numerical tests were performed on thirty-eight crack images to examine validity of the algorithm. Within the limited tests in the present study, the proposed algorithm was revealed as accurately recognizing the crack patterns when compared to those classified by a human expert

Self-Diagnosis of Fracture in Carbon Fiber Composite Mortar

In the research for giving self-diagnosing capability, conductive mortar intermixed with cokes and milled carbon fiber was produced. Then after examining change in the value of electric resistance dsand AE characteristics before and after the occurrence of cracks at each weight-stage, the correlations of each factors were analyzed