Buy article PDF
The purchased file will be sent to you
via email after the payment is completed.
US$ 35
|
Structural Engineering and Mechanics Volume 16, Number 1, July 2003 , pages 83-99 DOI: https://doi.org/10.12989/sem.2003.16.1.083 |
|
|
|
Prediction of concrete strength using serial functional network model |
||
S. Rajasekaran and Seung-Chang Lee
|
||
| 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). | ||
| Key Words | ||
| functional network; prediction; concrete strength; error function - minimization - Lagrangian. | ||
| Address | ||
| Department of Civil Engineering, PSG College of Technology, Coimbatore 641 004, Tamil Nadu, India Research and Development Center, Hyundai Development Company, Seoul 135-894, Korea | ||