The main aim of this article was to determine blemishes of concrete surfaces and divide those surfaces according to following methods provided by two documents and by authors proposed image scanning method - "Image". The first method was CIB Report No. 24 "Tolerances on blemishes of concrete". This method enables to evaluate concrete surfaces according to their visual appearance by using certain reference cards. The second method was GOST 13015.0-83. This method enables to evaluate the concrete surfaces according to their biggest dimension of the blemishes. The third, authors proposed, method was "Image". Latter method is based on the free source computer program.It helps to establish the quantity and the dimensions of the blemishes in the desired scale. Authors suggested to imply a ration between blemishes area and the all specimen's area as a factor for evaluation of concrete surface quality. Three different concrete compositions were made: BA1, BA7 and BA8. Also, five different formworks were used: wood impregnated with polymeric oil [WPO], wood covered with rubber [WCR], sawn timber [ST], metal [M] and plastic [P] formworks. Following parameters of the obtained results were calculated: mean value, dispersion, standard deviation and the coefficient of variation. Also maximum and minimum values of experimental results are given. Intervals of the experimental results are provided for each specimen with the biggest possibility.
Manually inspecting concrete surface defects e.g., cracks and air pockets is not always reliable. Also, it is labor-intensive. In order to overcome these limitations, automated inspection using image processing techniques was proposed. However, the current work can only detect defects in an image without the ability of evaluating them. This paper presents a novel approach for automatically assessing the impact of two common surface defects i.e., air pockets and discoloration. These two defects are first located using the developed detection methods. Their attributes, such as the number of air pockets and the area of discoloration regions, are then retrieved to calculate defects' visual impact ratios - VIRs. The appropriate threshold values for these VIRs are selected through a manual rating survey. This way, for a given concrete surface image, its quality in terms of air pockets and discoloration can be automatically measured by judging whether their VIRs are below the threshold values or not. The method presented in this paper was implemented in C++ and a database of concrete surface images was tested to validate its performance