Analysis method of apparent quality of fair-faced concrete based on convolutional neural network machine learning

Abstract

Fair-faced concrete has attracted much attention because of its noble texture, but the evaluation of its appearance quality often depends on personal subjective feelings (especially color difference) or complex image processing means, which hinders its development. Therefore, the accurate and objective evaluation of appearance quality is the key to fair-faced concrete engineering. This paper proposed a machine learning method to analyze the appearance quality of fair-faced concrete. Firstly, images of fair-faced concrete were collected by UAV (Unmanned Aerial Vehicle) or scanner for pretreatment. Then, based on the convolutional neural network algorithm, a visual quality database of fair-faced concrete including 2463 groups of information was established, and it was trained to recognize color difference and bubbles on the surface of fair-faced concrete pictures. The performance of the model is evaluated and optimized. The influence of environmental factors such as light intensity and surface moisture on the model is analyzed. Finally, the machine learning method is used to analyze the appearance quality of fair-faced concrete in engineering site. Compared with the traditional analysis methods, the accuracy of stomatal area rate and maximum stomatal diameter recognition was increased from 79.34 % to 93.83 %, and the accuracy of color difference recognition was increased by more than 10 %. At the same time, the identification method in this study still has strong robustness under light and surface moisture variation. The research results can provide useful thinking for evaluating fair-faced concrete engineering.