Tyurina V.S.

Candidate of Technical Sciences (Ph.D), Associate Professor, Don State Technical University, Department «Structural Mechanics and Theory of Structures»

Experience of concreting a massive monolithic foundation slab

https://doi.org/10.58224/2618-7183-2025-8-5-2
Аннотация
The large number of recipe and technological factors affecting the stress-strain state of concrete in the initial period of massive monolithic structures erection predetermines the expediency of using modeling of temperature fields and stresses with software packages based on analytical and numerical solutions when developing technological regulations for concreting. Improving the algorithm for calculating temperature fields and stresses taking into account the kinetics of concrete heat release, heat exchange conditions, ambient temperature and the stages of construction of structures is a pressing task. A comparison was made of calculated, laboratory and natural values of some parameters when concreting a foundation slab with a volume of 1642 m3, a surface area of 821 m2, and a thickness of 2 m. Concreting was completed in 13.5 hours with an average intensity of concrete mix placement of 122 m3/h, and a peak intensity of up to 240 m3/h. A method for calculating temperature fields and stresses taking into account the staged nature of construction has been developed in the MATLAB environment. It does not require rebuilding the geometry of the finite element model, adding nodes and elements during the process of laying new layers, and allows for the correct consideration of the dependence of the strength and deformation properties of concrete on the degree of its maturity. The results of calculated and measured temperature values excluding heating from solar radiation showed a discrepancy of up to 10 °C on the upper surface at some points in time. Some discrepancy between the calculated and experimental values of stresses and deformations with a qualitative coincidence in the nature of the curves is due to the neglection of shrinkage and rapid creep of concrete and poor study of the deformation properties of concrete with additives based on polycarboxylate esters at an early age.
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Predicting the risk of early cracking in massive monolithic foundation slabs using artificial intelligence algorithms

https://doi.org/10.58224/2618-7183-2025-8-1-6
Аннотация
The article presents a study of the application of artificial intelligence algorithms in predicting the risk of early cracking in massive reinforced concrete structures using monolithic foundation slabs as an example. The current experience of using algorithms such as convolutional neural networks, deep learning tools (YOLOv5 model) for crack detection at various stages of the life cycle of massive reinforced concrete structures is analyzed. The causes of crack formation, physical and mechanical processes, including cement hydration are considered.
A model has been developed that predicts the magnitude of the tensile stress level in monolithic foundation slabs during construction, based on CatBoost using Python, allowing to predict the risks of early cracking with an accuracy of up to 98%.
The model was trained on synthetic data containing various design parameters and material properties, including the geometric dimensions of the slabs, the temperature on the upper surface, the heat transfer coefficient on the upper surface, the curing rate, the class of concrete and the characteristics of the soil base. Statistical analysis of the data was performed, a correlation matrix was constructed. Practical and predicted values of the model were visualized in the form of a scatter plot. The most significant parameters influencing the risk of early cracking in massive monolithic foundation slabs were obtained. The constructed model passed quality assessment according to three metrics: MAE=0.0011; MSE=4.038; MAPE=0.0014.
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