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Journal of Engineering
Volume 2013 (2013), Article ID 946829, 5 pages
Research Article

The Prediction of Concrete Temperature during Curing Using Regression and Artificial Neural Network

1Department of Geology, Engineering Faculty, Science and Research Branch, Islamic Azad University, Poonak Square, Tehran, Iran
2Department of Mining Engineering, Engineering Faculty, Science and Research Branch, Islamic Azad University, Toward Hesarak, End of Ashrafi Esfahani, Poonak Square, Tehran 1477893855, Iran

Received 5 December 2012; Accepted 12 February 2013

Academic Editor: İlker B. Topçu

Copyright © 2013 Zahra Najafi and Kaveh Ahangari. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Cement hydration plays a vital role in the temperature development of early-age concrete due to the heat generation. Concrete temperature affects the workability, and its measurement is an important element in any quality control program. In this regard, a method, which estimates the concrete temperature during curing, is very valuable. In this paper, multivariable regression and neural network methods were used for estimating concrete temperature. In order to achieve this purpose, ten laboratory cylindrical specimens were prepared under controlled situation, and concrete temperature was measured by thermistors existent in vibrating wire strain gauges. Input data variables consist of time (hour), environment temperature, water to cement ratio, aggregate content, height, and specimen diameter. Concrete temperature has been measured in ten different concrete specimens. Nonlinear regression achieved the determined coefficient ( ) of 0.873. By using the same input set, the artificial neural network predicted concrete temperature with higher of 0.999. The results show that artificial neural network method significantly can be used to predict concrete temperature when regression results do not have appropriate accuracy.