Browsing by Author "Elshaeva, Diana"

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Citation - WoS: 2
Citation - Scopus: 2
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%.
Citation - WoS: 2
Citation - Scopus: 2
Properties and Structure of Functional Concrete Mixtures Modified With River Shell Powder
(C Ej Publishing Group, 2024) Stel'makh, Sergey A.; Shcherban, Evgenii M.; Beskopylny, Alexey N.; Hiep, Nguyen Quang; Song, Yamin; Elshaeva, Diana; Chernil'nik, Andrei; Aksoylu, Ceyhun
The recycling of the aquaculture waste into clam powder reduces solid emissions and natural resources, which is important for Portland cement production. This study determines the feasibility of using recycled river shell waste as a partial replacement for cement in concrete technology. The study used normative methods and optical microscopy; the properties of cement mixtures, such as normal consistency, setting time (ST), compressive and flexural strength, were studied. Research findings have shown that the inclusion of river shell powder (RSP) in cement mixtures can reduce water demand and a decrease in setting time with increasing RSP content. It was also found that the strength of the cement mixture can be maintained with an RSP content of up to 10%. The following properties of the concrete were determined: workability, compressive strength (CS), and water absorption. Using RSP as a partial replacement for cement has been proven to elevate the slump of the fresh concrete cone. CS is maintained at a level comparable to the control composition, with an RSP content of no more than 8%, and water-absorbing is reduced by 7.31%. This study created new compositions, and the links between the ingredients, properties, and structure of cement composites modified with river shell powder were investigated. Additionally, the properties of the structure-formation process of these modified composites were studied.