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Study on Adiabatic Temperature Rise Reflecting Hydration Degree of Concrete

机译:混凝土绝热温升反映水化度的研究

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The thermal model and the relevant parameters of concrete are the most important issues to study the space-time characteristics of temperature field, which are also the theoretical foundation of temperature control and crack prevention for the mass concrete structures. In this research, the improved adiabatic temperature rise test is carried out, and the temperature variation of fly ash concrete is analyzed. Furthermore, a thermal model of concrete considering the hydration degree is established based on the existing achievements. Meanwhile, the thermal conductivity and specific heat of concrete are measured via three approaches by treating the parameters as constant values, by computing the parameters as variables of the degree of hydration, and by back-analyzing the parameters through BP neural network. Finally, the thermal parameters determined by different methodologies are substituted into the thermal model, respectively, and the finite element analysis of the concrete specimen is performed. By comparing simulated temperatures with various measured results, it can be found that the numerical analysis results of parameters calculated by BP neural network are closest to the measured values in the whole curing ages. Therefore, BP neural network method is an effective way to calculate the thermal parameters, and BP inversion algorithm provides a new way for accurately study the temperature profile of mass concrete structures.
机译:混凝土的热模型和相关参数是研究温度场时空特性的最重要问题,也是大体积混凝土结构温度控制和防裂的理论基础。在这项研究中,进行了改进的绝热温升试验,并分析了粉煤灰混凝土的温度变化。在此基础上,建立了考虑水化程度的混凝土热模型。同时,通过将参数视为恒定值,通过将参数计算为水合度变量以及通过BP神经网络对参数进行反分析,通过三种方法来测量混凝土的导热率和比热。最后,将用不同方法确定的热参数分别代入热模型,并对混凝土试件进行有限元分析。通过将模拟温度与各种测量结果进行比较,可以发现,在整个固化过程中,由BP神经网络计算的参数的数值分析结果最接近于测量值。因此,BP神经网络方法是计算热工参数的有效方法,而BP反演算法为准确研究大体积混凝土结构的温度分布提供了新的途径。

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