Unsupervised Neural Network Approach to Frame Analysis of Conventional Buildings

• 作者: Alejandro R. Zambrano Lácides R. Pinto
• 来源期刊: 通讯、网络与系统学国际期刊(英文)
• 学科: 医学
• 关键词: Structural Analysis Neural Networks UNSUPERVISED Training END MOMENTS ROTATIONAL END MOMENTS
• 年，卷（期）: 2014,（7）
• 页码范围: 203-211
• 页数: 9页
• 分类号: R73

摘要

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.