A comparative study of deep learning methods on flexural buckling load prediction of aluminum alloy columns

In recent years, aluminum alloy columns have been widely used in construction fields. This is due to the light weight of aluminum alloys, high corrosion resistance, long life, low maintenance costs, the possibility of recovery, versatility of the metal and the possibility to obtain endless variety of profiles has many advantages. The calculation of the critical buckling loads of the columns is the most important issue. However, it is known that heat treated aluminum alloys have higher proof stress yield strength than non-heat treated aluminum alloys. In this study, buckling load estimation of heat treated aluminum alloy columns is made by using deep learning method and soft computing techniques and laboratory test results are compared. Sequential Model is used while using deep learning method. Adam, Adamax, Nadam, Adadelta, Adagrad, RMSProp and SGD and the optimizer functions of deep learning are evaluated separately. In addition, the results are evaluated using both MAE and MSE Loss functions for each optimizer. As a result of the study, it is understood that the optimizer and loss functions used together are more successful when estimating value for the dataset using deep learning model.