LAUSR

Due to the influence of temperature changes or temperature gradients in the construction process of mass concrete, temperature cracks will occur in the concrete. In order to achieve a reasonable prediction of the temperature change of the mass concrete during the construction process and accurately obtain the temperature change trend, this paper attempts to construct a CART prediction model based on the big data processing technology based on the characteristics of the temperature change of the mass concrete. This paper introduces in detail how to use data processing methods such as outlier identification, missing value filling and random error elimination to improve data quality, as well as the method for constructing the CART prediction model, and combines engineering examples to demonstrate the feasibility of the model method. The results show that the model and method can better predict the temperature change of mass concrete. It has high prediction accuracy and can provide necessary guidance for practical engineering.