Structural Engineering and Mechanics

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Prediction of concrete strength using serial functional network model

  • Rajasekaran, S. (Department of Civil Engineering, PSG College of Technology) ;
  • Lee, Seung-Chang (Research and Development Center, Hyundai Development Company)
  • Received : 2003.01.23
  • Accepted : 2003.05.07
  • Published : 2003.07.25
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Abstract

The aim of this paper is to develop the ISCOSTFUN (Intelligent System for Prediction of Concrete Strength by Functional Networks) in order to provide in-place strength information of the concrete to facilitate concrete from removal and scheduling for construction. For this purpose, the system is developed using Functional Network (FN) by learning functions instead of weights as in Artificial Neural Networks (ANN). In serial functional network, the functions are trained from enough input-output data and the input for one functional network is the output of the other functional network. Using ISCOSTFUN it is possible to predict early strength as well as 7-day and 28-day strength of concrete. Altogether seven functional networks are used for prediction of strength development. This study shows that ISCOSTFUN using functional network is very efficient for predicting the compressive strength development of concrete and it takes less computer time as compared to well known Back Propagation Neural Network (BPN).

Keywords

References

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