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Journal of Construction Engineering
Volume 2013 (2013), Article ID 380693, 8 pages
http://dx.doi.org/10.1155/2013/380693
Research Article

Estimating Strain Changes in Concrete during Curing Using Regression and Artificial Neural Network

1Department of Mining Engineering, Engineering Faculty, Science and Research Branch, Islamic Azad University, Toward Hesarak, End of Ashrafi Esfahani, Poonak Square, P.O. Box 14515/775 & 14155/4933, Tehran 1477893855, Iran
2Department of Geology, Science and Research Branch, Islamic Azad University, Toward Hesarak, End of Ashrafi Esfahani, Poonak Square, P.O. Box 14515/775 & 14155/4933, Tehran 1477893855, Iran

Received 14 December 2012; Revised 16 March 2013; Accepted 1 April 2013

Academic Editor: Anaclet Turatsinze

Copyright © 2013 Kaveh Ahangari et al. 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.

Abstract

Due to the cement hydration heat, concrete deforms during curing. These deformations may lead to cracks in the concrete. Therefore, a method which estimates the strain during curing is very valuable. In this research, two methods of multivariable regression and neural network were studied with the aim of estimating strain changes in concrete. For this purpose, laboratory cylindrical specimens were prepared under controlled situation at first and then vibration wire strain gauges equipped with thermistors were placed inside each sample to measure the deformations. Two different groups of input data were used in which variables included time, environment temperature, concrete temperature, water-to-cement ratio, aggregate content, height, and specimen diameter. CEM I, 42.5 R was utilized in set (I) and strain changes have been measured in six concrete specimens. In set (II) CEM II, 52.5 R was employed and strain changes were measured in three different specimens in which the diameter was held constant. The best multivariate regression equations calculated the determined coefficients at 0.804 and 0.82 for sets (I) and (II), whereas the artificial neural networks predicted the strain with higher of 1 and 0.996. Results show that the neural network method can be utilized as an efficient tool for estimating concrete strain during curing.