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

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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.

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68-71

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February 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] Wu Zhongru: Safety monitoring theory and its application of hydraulic structures (Higher Education Press, Beijing, China, 2003).

Google Scholar

[2] Yang Jie, Wu Zhongru and Gu Chongshi, in: BP neural network model of the dam deformation monitoring and prediction research, The journal of Xi'an University of Technology, Vol. 17(2001), pp.25-29.

Google Scholar

[3] Shen Xunlong, Wang Chao and Han Yanhui, in: Based on mass concrete temperature prediction and control of neural networks, The journal of Hefei University of Technology (Natural Science), Vol. 25(2002), pp.758-763.

Google Scholar

[4] Zhu Bofang: Mass concrete thermal stress and temperature control (China Electric Power Press, Beijing, China, 1999).

Google Scholar

[5] Liu Dandan, Wang Jun and Huang Yaoying, in: Study of concrete dam temperature statistical model during construction, The journal of Water resources and power, Vol. 30(2012), pp.76-77.

Google Scholar