# Prediction of Thermal Cracking During Construction of Massive Monolithic Structures
## 1 Introduction  
One of the main factors that worsen the serviceability and durability of massive monolithic structures are early age cracks, which are usually caused by thermal stresses that arise during construction. The reason for the increase in temperature and the uneven distribution of it between the layers of concrete is the heat of hydration, as well as the low thermal conductivity of the mixture, which significantly slows down the natural cooling process [1, 2]. Machine learning is currently widely used to predict and detect temperature cracks in the early period of hardening [3, 4, 5, 6, 7]. In these works, four models are proposed, the most accurate of which is the genetic programming model, which provides transparency and the possibility of practical application. The study [8] analyzed theoretical and experimental data on the correlation of digital images. Digital image correlation is a non-destructive testing method, which consists of three stages. The first stage is image creation for the building structure being examined. Then the image is saved in a certain digital format. Next, it is analyzed to obtain the necessary information about the stresses and deformationsin the object being examined. The method is designed to analyze crack formation and destruction of building structure elements. The application of machine learning methods in the problem of predicting the risk of early cracking in massive monolithic structures requires an extensive experimental base and is possible only for typical structures of a simple shape (flat foundation slabs, walls) [9]. The publication [10] presents a numerical and experimental study, during which the ConcreteWorks (CW) program was used to predict the thermal characteristics of concrete in order to prevent cracks. The temperature change profile, temperature difference, maturity, and compressive strength of the mixtures were predicted and compared with the data measured by monitoring the bridge foundation in real time. CW predictions coincide with the measured data. The authors of the articles [11; 12; 13; 14] present a comprehensive approach to solving the problem of thermal cracking at an early age by rational selection of concrete mixtures. This approach includes finite element modeling of the heat generation and heat transfer processes in concrete, as well as optimization methods. An effective genetic algorithm is used as an optimization method. As a result, the authors manage to minimize the risk of early cracking and reduce stresses to levels that do not exceed the strength characteristics of concrete. The use of optimization methods in the design of concrete mixtures leads to a noticeable decrease in the maximum temperature in structures, as well as the level of tensile stresses. Thermal stress analysis requires an accurate prediction of concrete properties, but this is a difficult task since some of the properties of concrete change dramatically at an early age depending on the conditions of its hardening [15; 16]. Currently, there is a general trend towards using the finite element method to solve this problem. This method is used in civil engineering and for the design of massive structures subject to uneven heating [17; 18]. Since the input data for this application of this method are not always available in the typical activities of engineers, simple analytical methods are still used. However, such methods are only applicable to structures of a simple shape and simple manufacturing conditions [19; 20]. For structures of a complex shape, finite element modeling is the most effective way to predict the thermal stress-strain state and risk of early cracking. At the same time, most existing software packages (ANSYS, Sofistik, Abaqus, Nasiran, etc.) by default, without modification and creation of custom extensions, do not take into account the change in the strength and elastic modulus of the material over time. The possibilities for modifying existing finite element method software are severely limited.