Durability as integral characteristic of concrete

Suleymanova, Lyudmila; Pogorelova, Inna; Suleymanov, K. A.; Kirilenko, Svetlana V.; Марушко, Михаил

doi:10.1088/1757-899x/327/4/042127

Cited by 7 publications

(5 citation statements)

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“…Кладка стен из ячеистобетонных блоков представляет собой эффективный метод строительства наружных стен зданий в современной практике архитектуры и строительства. Отличительные черты данного материала, такие как высокие теплофизические характеристики и механическая прочность, открывают перед ним широкие перспективы применения [1][2][3][4].…”

Section: управление жизненным циклом здания на этапе эксплуатации с и...unclassified

Building Life Cycle Management at the Operation Stage Using Artificial Neural Network Models and Machine Learning

Suleymanova,

Obaydi

2024

Bulletin of Belgorod State Technological University Named After. V. G. Shukhov

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The use of artificial neural networks and machine learning methods for the analysis of heat loss in buildings is of significant relevance in modern construction. These technologies are highly accurate and efficient in data processing. Artificial neural networks have the ability to analyze vast amounts of information and identify complex patterns, which significantly increases the accuracy of determining heat loss in buildings. In turn, machine learning methods make it possible to take into account various influencing factors, such as geographic location and meteorological conditions, thereby making a significant contribution to improving the quality of analytical results. Such approaches provide more reliable and accurate conclusions, which is critical for effective energy management and reducing heat loss in buildings. In this article, the authors conducted a study of heat losses of buildings and their prediction at the operational stage using artificial neural networks and machine learning methods. The technique is based on the analysis of data on heat loss and their relationship with various building parameters. Forecasting was carried out using artificial neural networks in the Statistica software package and the machine learning method based on the scikit-learn library. The proposed approach allows you to effectively manage the energy consumption of a building, optimizing its energy efficiency and improving the life cycle management of a capital construction project. The results demonstrate the high accuracy and convergence of the model with actual values, as well as its ability to predict performance.

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Section: управление жизненным циклом здания на этапе эксплуатации с и...unclassified

Building Life Cycle Management at the Operation Stage Using Artificial Neural Network Models and Machine Learning

Suleymanova,

Obaydi

2024

Bulletin of Belgorod State Technological University Named After. V. G. Shukhov

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“…Введение. В современном строительстве одним из важнейших материалов являются ячеистые бетоны, обладающие уникальными свойствами, такими как низкая плотность, высокая теплоизоляция и прочность [1][2][3][4]. Однако для эффективного проектирования и использования ячеистых бетонов необходимо иметь подробное представление об их пористой структуре.…”

Section: исследование макропористой структуры ячеистого бетонаunclassified

Study of the Macroporous Structure of Cellular Concrete

Suleymanov,

Lesovik,

Pogorelova

et al. 2024

Bulletin of Belgorod State Technological University Named After. V. G. Shukhov

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The method of image analysis of the front surface of cellular concrete is an innovative way to obtain detailed and reliable data on the structure of the material at both the macro and micro levels. This characteristic is key to a deep understanding of the characteristics of the material. The main advantage of this method is its non-contact nature. Unlike traditional methods, which may require destruction of the sample during analysis, image analysis allows examination without any impact on the integrity of the material, which is especially important since this approach ensures that the sample is preserved for subsequent studies. The article presents a method developed by the authors for determining the porosity of cellular concrete. A feature of this method is the filling of the pores of the front surface with crushed amorphous carbon, followed by obtaining an image of the front surface using a digital camera, and then its processing and analysis. The results obtained using the developed method show high convergence with the results obtained using traditional methods for analyzing images of porous structures. Thus, the use of image analysis method represents a promising approach to determining the porosity of cellular concrete, providing high accuracy of data with minimal impact on the object of study.

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“…Point 1 It should be noted that a distinctive feature of the microstructure of the mode I concrete sample is the presence of ettringite crystals, which are absent in the microstructure of the mode II concrete sample. The absence of ettringite in the structure of the sample tested in mode II can be explained by the ettringite transition to monosulfate (according to [4,6,15,[19][20][21][22][23][24][25][26][27][28][32][33][34][35][36][37], complete decomposition of ettringite occurs at temperatures around 60-80 °C).…”

Section: Mode I Mode Iimentioning

confidence: 99%

“…According to IAEA documents [1,2], one of the priority research areas in the field of used nuclear fuel storage facilities construction is research into the reinforced concrete structure degradation effects and mechanisms in order to ensure their safe operation [3][4][5][6]. Understanding the mechanisms of concrete degradation in relation to such buildings is inextricably connected to the research into the operating conditions of these structures and external influences leading to accelerated aging of concrete and reinforcing steel [1].…”

Section: Introductionmentioning

confidence: 99%

“…As can be seen from the table above, the impact of stressors leading to concrete degradation is usually associated with a combination of various internal and external influences. Therefore, it should be taken into account at the design stage of deep-burial storage facilities in order to ensure the resistance of concrete structures to intensive degradation during the design life of the building [1,2,4,[6][7][8]. The cumulative influence of factors leading to con-also occur in deeper layers in the presence of cracks or as a result of prolonged interaction with an aggressive environment [1,4].…”

Section: Introductionmentioning

confidence: 99%

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Concrete Carbonation of Deep Burial Storage Constructions under Model Aging Conditions

Medvedev,

Pustovgar,

Adamtsevich

et al. 2023

Buildings

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To ensure the safe operation of concrete structures of deep-burial storages, it is necessary to research the degradation mechanisms of such structures. Concrete carbonation is one of the key factors determining the service life of concrete structures. Existing methods for the concrete carbonation process research at various stages of the building structure life cycle make it possible to conduct model tests in the shortest possible time by simulating the operational factors influencing the corrosion process development. The authors carried out model tests of concrete of deep-burial storages using the method of accelerated concrete carbonation and by taking into account the effects of elevated temperatures. When exposed to elevated temperatures during carbonation, concrete samples exhibit a decrease in compressive strength in the first 56 days of testing by an average of 1.6 MPa. However, by the end of the tests (168 days), the strength of concrete samples at elevated temperatures is on average 4 MPa higher. The microstructure and carbonation dynamics were studied by XRD, TGA and SEM. The data obtained in the research can be used to develop models for predicting the service life of concrete structures of deep-burial storages.

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Durability as integral characteristic of concrete

Cited by 7 publications

References 0 publications

Building Life Cycle Management at the Operation Stage Using Artificial Neural Network Models and Machine Learning

Building Life Cycle Management at the Operation Stage Using Artificial Neural Network Models and Machine Learning

Study of the Macroporous Structure of Cellular Concrete

Concrete Carbonation of Deep Burial Storage Constructions under Model Aging Conditions

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