TITLE:
Unsupervised Neural Network Approach to Frame Analysis of Conventional Buildings
AUTHORS:
Lácides R. Pinto, Alejandro R. Zambrano
KEYWORDS:
Structural Analysis, Neural Networks, Unsupervised Training, End Moments, Rotational End Moments
JOURNAL NAME:
International Journal of Communications, Network and System Sciences,
Vol.7 No.7,
July
9,
2014
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 rth 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.