• Journal of Korean Society of Steel Construction
• /
• v.16 no.6 s.73
• /
• pp.847-855
• /
• 2004

The criterion or specification on fatigue design has been generally based on fatigue strength curve derived by tests on specimens with varying thickness (10-30mm). Making the plate thicker, however, also decreases fatigue strength. It has been noted from the test results and the results of the analysis by fracture mechanics that the effect of thickness cannot be bypassed. From the several fatigue strength curves of specimen tests, modification of fatigue strength on plate thickness has been proposed. In this study, fatigue tests on SM520C-TMC were carried out, and the effects of thickness were evaluated. Finally, in consideration of the thickness, the modification of fatigue strength was derived. Comparing the results of this paper with those of previous studies, an outline of the behavior obtained is similar to previous ones, but the rate of decrease is smaller.

• Steel and Composite Structures
• /
• v.9 no.6
• /
• pp.557-568
• /
• 2009

Fatigue tests for extremely thick plates require a great deal of manufacturing time and are expensive to perform. Therefore, if predictions could be made through simulation models such as an artificial neural network (ANN), manufacturing time and costs could be greatly reduced. In order to verify the effects of fatigue strength depending on the various factors in SM520C-TMC steels, this study constructed an ANN and conducted the learning process using the parameters of calculated stress concentration factor, thickness and input heat energy, etc. The results showed that the ANN could be applied to the prediction of fatigue life.