## 1 Introduction  
With the rapid development of damming technology, the construction scale of concrete dams is also increasing, resulting in higher dam heights, faster construction speeds, and larger volumes of concrete. Owing to the vast quantity of heat released by cement hydration and the poor thermal conductivity of concrete, heat in the core of dam concrete in unlikely to dissipate in time without effective cooling measures, resulting in high thermal stress and even cracks. Temperature control of mass concrete was, is, and remains to be the key concern of concrete dams. Fly ash and superplasticizer are often used to reduce the heat of cement hydration. Limestone fines added as a filler to concrete also effectively suppress the adiabatic temperature rise, but the degree of hydration of the cement is improved owing to related chemical reactions, thus strengthening the concrete [1]. The application of low-heat cement fundamentally reduces the heat of cement hydration; its successful utilization in Hoover dam further promoted the development of low-heat cement concrete [2]. However, a postcooling system is also necessary to prevent thermal cracks. To achieve precise temperature control, an accurate real-time prediction system is needed. Temperature rises in concrete are caused by the hydration of cementitious materials. Over recent years, many studies were carried out regarding the hydration mechanism and the temperature development of concrete. The adiabatic temperature rise measured by adiabatic calorimetry is a common way of reflecting the heat-release process of cement hydration; on this basis, many methods for temperature prediction were developed. As a simplified finite difference method proposed in the 1930s, [PERSON]'s method mainly considers heat generation and heat transfer of concrete, playing an important role in the era of manual calculation and performing better than the traditional empirical methods [3,4]. However, [PERSON]'s method assumes that the thermal conductivity and specific heat of all nodes are the same and constant, which is not consistent with reality. With the development of the finite element method (FEM), the spatial and temporal distributions of a temperature field could be obtained [5, 6, 7, 8, 9, 10, 11, 12, 13]. Owing to the related properties assigned to each element, the temperature variations of concrete are simulated more accurately using this method, but the more refined the model is, the longer it takes to calculate. In addition, a number of researchers developed a numerical simulation of the exothermic process of cement hydration according to the chemical reaction of each composition [14, 15, 16, 17], with the microstructure model being established based on molecular dynamics to simulate cement hydration in a certain environment [18]. Alongside the progression of experimental methods and the deepening of applicable research, a series of micromonitoring experiments regarding cement hydration were also performed. Combined with the micromonitoring results, changes in cement hydration properties, such as temperature and elastic modulus, were analyzed [19, 20, 21, 22]. The arrival of the information age emphasized the significance of data utilization. Usually, it is suitable for extremely complex problems to find hidden rules in large amounts of data through mathematical statistics or deep learning algorithms, among others. Naturally, data mining technology to predict concrete temperature and other properties was also realized [23, 24, 25], but was not greatly integrated or developed in terms of engineering practice. Many predictions were achieved in regard to concrete temperature, however, because the models used in the calculation are often complex and time-consuming, such as using FEM for a comprehensive simulation, it is not easy to make fast, real-time predictions during the process of construction. Via in situ monitoring of the Baihetan arch dam and Wudongde arch dam, this paper studies the relevant data of dam concrete at an early age.