Parametric analysis of collapse load factor of planar frames

Abstract

The plastic analysis and determining the collapse load factor (CLF) of frames have received much attention in all kinematical methods applied for designing these frames. CLF in frames depends on many effective parameters such as length of the bay, the height of story, loads, and plastic moments. As the number of these parameters increases, the analysis becomes more complex and time-consuming. In this regard, artificial neural networks (ANNs) have been found many applications in prediction and optimization of ill-defined problems. In the present paper, two planar frames were considered and analyzed as examples for modeling via ANN. In order to develop the ANNs, results of thirty samples were gathered from every two examples. Parameters including the length of the bay, the height of story, loads, and plastic moments were regarded as inputs and the CLF was considered as the output of the networks. After developing the networks, the model function was generated and the effects of the input parameters were investigated on the CLF of frames. The results showed tuning networks are finely and adjusting their parameters would lead to very good and acceptable results for the design of frames.