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.
Highlights
IntroductionGreat strides have been taken in developing frame analysis. Throughout the evolution of structural science, most of the work has been done regarding frame analysis
In the past decades, great strides have been taken in developing frame analysis
In the frame analysis developed in the neural network model, the final end moments Mik and M ki are determined by finding out the different components Mi′k, M k′i and Mi′k′ separately, and adding them up as per Equations (9) and (10)
Summary
Great strides have been taken in developing frame analysis. Throughout the evolution of structural science, most of the work has been done regarding frame analysis. In order to understand the achievement of moment distribution approach, it would be helpful reviewing developments of elasticity theory and its fundamental principles, as it applies to statically indeterminate structures. M. Turner proposed in 1959 the Direct Stiffness Method of structural analysis [6], one that has undergone the most dramatic changes: an efficient and general computer-based implementation of the incipient Finite Element Method (FEM). Turner proposed in 1959 the Direct Stiffness Method of structural analysis [6], one that has undergone the most dramatic changes: an efficient and general computer-based implementation of the incipient Finite Element Method (FEM) Nowadays it can find other models like the Finite Volume Model [7], used to solve fluids dynamics problems. This paper evaluates a neural network approach on frame analysis using an unsupervised algorithm.
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