Publication Type:

Journal Article

Source:

Archives of Civil Engineering, Volume 53, Number 4, p.639-662 (2007)

URL:

https://www.infona.pl/resource/bwmeta1.element.baztech-article-BTB2-0043-0046

Keywords:

Backpropagation, error tolerance, failure load, Feedforward network, Modeling, neural network, Testing, Training

Abstract:

The analysis of infilled frames is complex due to the non-linearity involved as well as the large number of variables. Artificial Neural Network (ANN) has been found to be a tool that can accommodate the large number of variables and the nonlinear behaviour of the system. The ANN model was trained using the data available on failure load for the infilled frame under various conditions, generated analytically using equivalent strut method. The so trained model was tested for different set of input and output data obtained analytically as well as experimentally [10]. The agreement between the predicted and the actual results are found to be good. The results show that if the data for training is sufficient, the performance of the network will be satisfactory. The neural network approach is versatile since the size and scope of the input and output vectors can be increased to a large extent to meet the complexities.

Cite this Research Publication

Dr. Mini K. M. and Subramanian, K., “Neural Network Model for the Analysis of Infilled Framed Structures”, Archives of Civil Engineering, vol. 53, pp. 639-662, 2007.