Abstract: The calculation of buckling length factor (K) for steel
frames columns is a major and governing processes to determine the
dimensions steel frame columns cross sections during design. The
buckling length of steel frames columns has a direct effect on the cost
(weight) of using cross section. A new formula is required to
determine buckling length factor (K) by simplified way. In this
research a new formula for buckling length factor (K) was established
to determine by accurate method for a limited interval of columns
ends rigidity (GA, GB). The new formula can be used ease to
evaluate the buckling length factor without needing to complicated
equations or difficult charts.
Abstract: The main goal of the present work is to decrease the
computational burden for optimum design of steel frames with
frequency constraints using a new type of neural networks called
Wavelet Neural Network. It is contested to train a suitable neural
network for frequency approximation work as the analysis program.
The combination of wavelet theory and Neural Networks (NN)
has lead to the development of wavelet neural networks.
Wavelet neural networks are feed-forward networks using
wavelet as activation function. Wavelets are mathematical
functions within suitable inner parameters, which help them to
approximate arbitrary functions. WNN was used to predict the
frequency of the structures. In WNN a RAtional function with
Second order Poles (RASP) wavelet was used as a transfer
function. It is shown that the convergence speed was faster
than other neural networks. Also comparisons of WNN with
the embedded Artificial Neural Network (ANN) and with
approximate techniques and also with analytical solutions are
available in the literature.