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.
[1]
R. Lippmann,et al.
An introduction to computing with neural nets
,
1987,
IEEE ASSP Magazine.
[2]
Kyle A. Riding,et al.
Evaluation of Temperature Prediction Methods for Mass Concrete Members
,
2006
.
[3]
Okan Karahan,et al.
Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete
,
2009,
Adv. Eng. Softw..
[4]
Dilip Sarkar,et al.
Methods to speed up error back-propagation learning algorithm
,
1995,
CSUR.
[5]
P. F. Siew,et al.
Temperature and heat stress in a concrete column at early ages
,
2003
.