Browsing by Author "Razveeva, Irina"

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    Porosity Analysis and Thermal Conductivity Prediction of Non-Autoclaved Aerated Concrete Using Convolutional Neural Network and Numerical Modeling
    (MDPI, 2025) Beskopylny, Alexey N.; Shcherban', Evgenii M.; Stel'makh, Sergey A.; Elshaeva, Diana; Chernil'nik, Andrei; Razveeva, Irina; Ozkilic, Yasin Onuralp
    Currently, the visual study of the structure of building materials and products is gradually supplemented by intelligent algorithms based on computer vision technologies. These algorithms are powerful tools for the visual diagnostic analysis of materials and are of great importance in analyzing the quality of production processes and predicting their mechanical properties. This paper considers the process of analyzing the visual structure of non-autoclaved aerated concrete products, namely their porosity, using the YOLOv11 convolutional neural network, with a subsequent prediction of one of the most important properties-thermal conductivity. The object of this study is a database of images of aerated concrete samples obtained under laboratory conditions and under the same photography conditions, supplemented by using the author's augmentation algorithm (up to 100 photographs). The results of the porosity analysis, obtained in the form of a log-normal distribution of pore sizes, show that the developed computer vision model has a high accuracy of analyzing the porous structure of the material under study: Precision = 0.86 and Recall = 0.88 for detection; precision = 0.86 and recall = 0.91 for segmentation. The Hellinger and Kolmogorov-Smirnov statistical criteria, for determining the belonging of the real distribution and the one obtained using the intelligent algorithm to the same general population show high significance. Subsequent modeling of the material using the ANSYS 2024 R2 Material Designer module, taking into account the stochastic nature of the pore size, allowed us to predict the main characteristics-thermal conductivity and density. Comparison of the predicted results with real data showed an error less than 7%.