Vision-based Crack Identification on the Concrete Slab Surface using Fuzzy Reasoning Rules and Self-Organizing

Identifying cracks on the surface of concrete slab structure is important for structure stability maintenance. In order to avoid subjective visual inspection, it is necessary to develop an automated identification and measuring system by vision based method. Although there have been some intelligent computerized inspection methods, they are sensitive to noise due to the brightness contrast and objects such as forms and joints of certain size often falsely classified as cracks. In this paper, we propose a new fuzzy logic based image processing method that extracts cracks from concrete slab structure including small cracks that were often neglected as noise. We extract candidate crack areas by applying fuzzy method with three color channel values of concrete slab structure. Then further refinement processes are performed with Self Organizing Map algorithm and density based noise removal process to obtain basic crack characteristic attributes for further analysis. Experimental result verifies that the proposed method is sufficiently identified cracks with various sizes with high accuracy (97.3%) among 1319 ground truth cracks from 30 images

Non-destructive evaluation of the pull-off adhesion of concrete floor layers using rbf neural network

The interlayer bond is one of the primary qualities assessed during an inspection of floor concrete workmanship. The measure of this bond is the value of pull-off adhesion f b determined in practice by the pull-off method. The drawback of this method is that the tested floor is damaged in each of the test points and then needs to be repaired. This drawback can be overcome by developing a way which will make it possible to test floors in any point without damaging them locally. In this paper it is proposed to evaluate the pull-off adhesion of the top layer to the base layer in concrete floors by means of the radial basis function (RBF) artificial neural network using the parameters evaluated by the non-destructive acoustic impulse response technique and the non-destructive optical laser triangulation method. Presented RBF neural network model is useful tool in the non-destructive evaluation of the pull-off adhesion of concrete floor layers without the need to damage the top layer fragment from the base