Study of the Concrete Block Temperature Intelligent Prediction Model during the Construction

Abstract

The explicit statistical model of concrete temperature variation is difficult to reasonably reflect the nonlinear relationship between the historical information and future information. This article is based on neural network intelligence tools and uses the neural network model to describe the concrete temperature variation during the construction. The relationships between the concrete temperature and initial temperature (pouring temperature), environmental temperature, the cement hydration heat temperature increase, water cooling effect and other factors are nonlinear. Establishing the neural network model of concrete temperature variation, exploring the historical temperature information could predict the future temperature information. Applying the intelligent prediction model to a construction project shows that when compared with the traditional explicit temperature statistical model, the temperature neural network prediction model established in this paper has obvious simplicity and superiority.