The application of optimization method of image segmentation to estimate the percentage content of concrete components is considered. Non-destructive testing methods (ultrasonic, magnetic, radiographic, image processing) are actively used to assess the condition of concrete structures and structures of long-term use. Recently, the share of studies of the mechanical properties of concrete based on the processing of images of sections of its samples has increased significantly. The relationship between the parameters obtained by digital image processing methods and the compressive strength of concrete is established on the basis of regression analysis. A method of segmentation of color images of test sample sections of concrete based on the Gaussian mixture method and level-sets model is developed. Based on the analysis of the differences in the color characteristics of the background and the object, it is concluded that they can be divided into two classes in the RGB color space. For this purpose appropriate training samples are created, which contain image pixel samples with typical features of the respective classes. The training sample consists of a set of feature vectors of image pixels. The parameters of the segmentation model have been adjusted. The experimental results of the segmentation of color images of sections of test concrete samples by the proposed method are presented. An analysis of the obtained results is carried out.

1. Basyigit, C.; Comak, B.; Kilincarslan, S.; Uncu, İ.S. Assessment of concrete compressive strength by image processing technique, Construction and Building Materials, 2012, 37, 526-532. https://doi.org/10.1016/j.conbuildmat.2012.07.055

2. Dogan, G.; Arslan, M.H.; Ceylan, M. Concrete compressive strength detection using image processing based new test method, Measurement, 2017, 109, 137-148. https://doi.org/10.1016/j.measurement.2017.05.051

3. Meihui, L.; Bo, Y.; Lin, W.; Yu, L.; Xiuyang, Z.; Jin, Z.; Liangliang, Z. The prediction of cement compressive strength based on gray level images and neural network. 3rd International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS), Jinzhou, China, 2016, pp. 103-108. https://doi.org/10.1109/ICCSS.2016.7586432

4. Dao, D.V.; Adeli, H.; Ly, H.-B.; Le, L.M.; Le, V.M.; Le, T.-T.; Pham, B.T. A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation. Sustainability, 2020, 12, 830. https://doi.org/10.3390/su12030830

5. Standard Test Method for Microscopical Determination of Parameters of the Air-Void System in Hardened Concrete, ASTM International, 2016, 15.

6. Powers, T. C. The air requirement of frost resistant concrete, Proceedings of the Highway Research Board, 1949, 29, 184-221.

7. Mehta, P. K.; Monteiro, P. J. M. Concrete: Microstructure, Properties, and Materials, 4th Edition, McGraww-Hill Education, 2014.

8. Wolter, S.; Uhre, F.A.H.; Hasholt, M.T.; Dahl, V.A.; Anton, F. Air void analysis of hardened concrete by means of photogrammetry, Construction and Building Material, 2019, 226, 953-964. https://doi.org/10.1016/j.conbuildmat.2019.07.203

9. Toumi, B.; Resheidat, M. A simple and low cost method for rapid assessment of air voids in hardened concrete, Asian Journal of Civil Engineering (Building and Housing), 2010, 11(3), 335-343.

10. Warren, C. Determination of Properties of Air Voids in Concrete. Bulletin No. 70, Highway Research Board, Washington, DC, 1953, 1-10.

11. Wawrzenczyk, J.; Kozak, W. A method of analyzing the porous microstructure in air-entrained concrete on the basis on 2D image analysis, Procedia Engineering, 2015, 108, 102-107. https://doi.org/10.1016/j.proeng.2015.06.124

12. Philleo, R.E. A Method for Analyzing Void Distribution In Air-Entrained Concrete, CCA, 1983, 2, 128-130. https://doi.org/10.1520/CCA10263J

13. Schouenborg, B.; Lindqvist, J.; Sandström, M. Air and air void structures in concrete - general overview and picture atlas, Nordtest project 1121-93, Engineering, 1995, 50.

14. Molendowska, A.; Wawrzenczyk, J.; Kowalczyk, H. Development of the Measuring Techniques for Estimating the Air Void System Parameters in Concrete Using 2D Analysis Method, Materials, 2020, 13, 428. https://doi.org/10.3390/ma13020428

15. Ojala, T.; Chen, Y.; Punkki, J.; Al-Neshawy, F. Characteristics of Entrained Air Voids in Hardened Concrete with the Method of Digital Image Analysis Coupled with Schwartz-Saltykov Conversion, Materials, 2021, 14 (9), 2439. https://doi.org/10.3390/ma14092439

16. Admixtures for concrete, mortar and grout - Test methods - Part 11: Determination of air void characteristics in hardened concrete, Danish Standard, DS/EN 480-11, 1998. 17. Pigeon, M.; Pleau, R. Durability of concrete in cold climates, E&FN SPON, 1995.

18. Basyigit, C.; Comak, B.; Kilincarslan, S. Estimated the amount of air void in concrete using image processing technique, 2nd International Balkans Conference on Challenges, 2013, pp. 495-502.

19. Joshi, S.K. On application of image processing: study of digital image processing techniques for concrete mixture images and its composition, International Journal of Engineering Research, 2014, 3(3), 1137-1146.

20. Song, Y., Shen, C.; Damiani, R.M.; Lange, D. A. Image-based restoration of the concrete void system using 2D-to-3D unfolding technique, Construction and Building Materials, 2021, 270, 121476. https://doi.org/10.1016/j.conbuildmat.2020.121476