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 (R 2) of 0.873. By using the same input set, the artificial neural network predicted concrete temperature with higher R 2 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.

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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.