The concept of sustainable development provides for the search for environmentally friendly alternatives to traditional materials and technologies that would reduce the amount of CO₂ emissions into the atmosphere, do not pollute the environment, and reduce energy costs and the cost of production processes. These technologies include the production of geopolymer concretes. The purpose of the study was a detailed in-depth analytical review of studies of the processes of structure formation and properties of geopolymer concretes in retrospect and the current state of the issue. Geopolymer concrete is a suitable, environmentally friendly and sustainable alternative to concrete based on ordinary Portland cement (OPC) with higher strength and deformation properties due to its more stable and denser aluminosilicate spatial microstructure. The properties and durability of geopolymer concretes depend on the composition of the mixture and the proportions of its components. A review of the mechanisms of structure formation, the main directions for the selection of compositions and processes of polymerization of geopolymer concretes has been made. The technologies of combined selection of the composition of geopolymer concrete, production of nanomodified geopolymer concrete, 3D printing of building structures from geopolymer concrete, and monitoring the state of structures using self-sensitive geopolymer concrete are considered. Geopolymer concrete with the optimal ratio of activator and binder has the best properties. Geopolymer concretes with partial replacement of OPC with aluminosilicate binder have a denser and more compact microstructure due to the formation of a large amount of calcium silicate hydrate, which provides improved strength, durability, less shrinkage, porosity and water absorption. An assessment of the potential reduction in greenhouse gas emissions from the production of geopolymer concrete compared to the production of OPC has been made. The potential of using geopolymer concretes in construction practice is assessed in detail.

Improving the efficiency and quality of construction mainly depends on the cost of building materials, which is about 55–65% of total capital-construction costs. The study aimed to obtain geopolymer fine-grained concrete with improved quality characteristics that meet the construction field’s sustainable development criteria and that have environmental friendliness, economic efficiency, and advantages over competing analogues. The dependences of strength characteristics on various compositions of geopolymer concrete were obtained. It was found that the most effective activator is a composition of NaOH and Na₂SiO₃ with a ratio of 1:2. The increase in the indicators of the obtained geopolymer concrete from the developed composition (4A) in relation to the base control (1X) was 17% in terms of compressive strength and 24% in tensile strength in bending. Polynomial equations were obtained showing the dependence of the change in the strength characteristics of geopolymer concrete on the individual influence of each of the activators. A significant effect of the composition of the alkaline activator on the strength characteristics of geopolymer fine-grained concrete was noted. The optimal temperature range of heat treatment of geopolymer concrete samples, contributing to the positive kinetics of compressive strength gain at the age of 28 days, was determined. The main technological and recipe parameters for obtaining geopolymers with the desired properties, which meet the ecology requirements and are efficient from the point of view of economics, were determined.

One of the unifying factors for all countries is the large consumption of chicken, and other, eggs in food and other types of economic activity. After using various types of eggs for their intended purpose, a large amount of waste accumulates in the form of eggshells. Currently, this problem exists and needs a non-trivial, original solution. The aim of the work was to fill the scientific gap in the direction of studying the microstructure formation of improved nano-modified environmentally-friendly concrete based on eggshell powder and obtaining a concrete composition for the manufacture of an industrial sample of such a material. An environmentally-friendly concrete was obtained, the characteristics of which were improved relative to standard concrete by modifying it with eggshell powder, for which the optimal dosage was determined. The most effective was the replacement of part of the cement with eggshell powder in the amount of 10%. The maximum increase in strength characteristics ranged from 8% to 11%. The modulus of elasticity increased by 4% compared to the control samples without eggshell powder. The maximum reduction in deformations under axial compression and tension in comparison with the control values ranged from 5% to 10%. The study of the composite’s microstructure nano-modified with eggshell powder, and an analysis of the changes occurring in this microstructure due to nano-modification, confirmed the improvement in characteristics and the optimal dosage of eggshell powder.

Some of the primary problems of construction are brittleness and low the mechanical properties of good thermal insulation materials. Heat-insulating foam concrete has a low thermal conductivity. However, it is practically impossible to transport it over long distances since corners are cracked during transportation, the structure is broken, and, in principle, the fragility of this material is a big problem for modern buildings. The purpose of this study was to develop a heat-insulating foam concrete with improved characteristics by experimentally selecting the optimal dosage of polypropylene fiber and a nanomodifying microsilica additive. Standard methods for determining the characteristics of fiber foam concrete were used as well as the method of optical microscopy to study the structure of the composite. It has been established that the use of polypropylene fiber with the optimal reinforcement range from 1% to 3% allows us to achieve an improvement in the mechanical and physical characteristics of fiber foam concrete. The optimal dosage of the nanomodifier introduced instead of a part of the binder (10%) and polypropylene fiber (2%) by weight of the binder was determined. The maximum values of increments in mechanical characteristics were 44% for compressive strength and 73% for tensile strength in bending. The values of the thermal conductivity coefficient at optimal dosages of the nanomodifier and fiber decreased by 9%. The absence of microcracking at the phase boundary between the polypropylene fiber and the hardened cement–sand matrix due to nanomodification was noted.

The partial replacement of the mineral components of concrete with natural renewable analogues in full possession of the performance characteristics of the final material, allows not only the concrete-production process to be made more environmentally friendly and inexpensive, but also to solve an important task for the agricultural industry, which is that associated with waste disposal. The scientific novelty of the work is in the obtaining of new concrete compositions by the partial replacement of coarse aggregate with a natural analogue in the form of a walnut shell, which has the maximum ratio of the strength of the composite to its density, as well as in identifying new dependencies of strength and density and their ratio on the amount of replacement of mineral coarse-aggregate walnut shell. The main goal of this article was to analyze the effect of composition factors on characteristics of concrete with partial replacement of large aggregates with walnut shells and to search for the optimal compound that would make it possible to obtain concrete with a minimum decrease in strength characteristics with a maximum decrease in concrete density. Cubes and prism laboratory samples were made from concrete of normal density with the replacement of coarse aggregate by 5, 10, 15, 20, 25 and 30%, by volume. The main mechanical properties, such as density, strength (compressive, tensile, tensile strength in bending) of the concrete samples were studied. The investigation used standard methods and scanning electron microscopy. An increase into strength characteristics up to 3.5%, as well as the maximum ratio of strength to density of concrete, was observed at a walnut-shell dosage of 5%. Effective partial replacement of coarse aggregate with walnut shells leads to a reduction in the consumption of crushed stone by up to 10% and a decrease in the mass of concrete by up to 6%.

The determination of mechanical properties for different building materials is a highly relevant and practical field of application for machine learning (ML) techniques within the construction sector. When working with vibrocentrifuged concrete products and structures, it is crucial to consider factors related to the impact of aggressive environments. Artificial intelligence methods can enhance the prediction of vibrocentrifuged concrete properties through the use of specialized machine learning algorithms for materials’ strength determination. The aim of this article is to establish and evaluate machine learning algorithms, specifically Linear Regression (LR), Support Vector Regression (SVR), Random Forest (RF), CatBoost (CB), for the prediction of compressive strength in vibrocentrifuged concrete under diverse aggressive operational conditions. This is achieved by utilizing a comprehensive database of experimental values obtained in laboratory settings. The following metrics were used to analyze the accuracy of the constructed regression models: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root-Mean-Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and coefficient of determination (R²). The average MAPE in the range from 2% (RF, CB) to 7% (LR, SVR) allowed us to draw conclusions about the possibility of using “smart” algorithms in the development of compositions and quality control of vibrocentrifuged concrete, which ultimately entails the improvement and acceleration of the construction and building materials manufacture. The best model, CatBoost, showed MAE = 0.89, MSE = 4.37, RMSE = 2.09, MAPE = 2% and R² = 0.94.