Unsupervised Neural Network Approach to Frame Analysis of Conventional Buildings

Lácides R. Pinto, Alejandro R. Zambrano

Universidad de La Guajira, UNIGUAJIRA, Riohacha, Colombia.
Universidad Nacional Experimental Politécnica, UNEXPO, Puerto Ordaz, Venezuela.

DOI: 10.4236/ijcns.2014.77022                

Abstract

In this paper, an Artificial Neural Network (ANN) model is used for the analysis of any type of conventional building frame under an arbitrary loading in terms of the rotational end moments of its members. This is achieved by training the network. The frame will deform so that all joints will rotate an angle. At the same time, a relative lateral sway will be produced at the r_th floor level, assuming that the effects of axial lengths of the bars of the structure are not altered. The issue of choosing an appropriate neural network structure and providing structural parameters to that network for training purposes is addressed by using an unsupervised algorithm. The model’s parameters, as well as the rotational variables, are investigated in order to get the most accurate results. The model is then evaluated by using the iteration method of frame analysis developed by Dr. G. Kani. In general, the new approach delivers better results compared to several commonly used methods of structural analysis.

Keywords

Structural Analysis, Neural Networks, Unsupervised Training, End Moments, Rotational End Moments

Conflicts of Interest

The authors declare no conflicts of interest.

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