Artificial Intelligence On Vortex-induced Vibration Performance Of ?-section Girder Of Cabel-stayed Bridge

?-section girder has a widespread application in cable-stayed bridges,for its simple and clear stress conditions and a high degree of standardization.However,the bluff body section of ?-section girder is prone to vortex induced vibration(VIV),which has a low critical wind speed and a high frequency of occurrence.VIV can affect the safety of bridge structures.Therefore,it is of great significance and value to study VIV.Due to the complex mechanics of VIV,it is difficult to find a unified vortex force model to solve the VIV response.Based on the existing wind tunnel test results and computational fluid dynamics(CFD),this paper use adopts the artificial neural network(ANN)method to identify the VIV performance of ?-section girder.The main contents of this paper are as follows.(1)In wind tunnel three sectional model with different aspect ratio were designed.First,a comprehensive VIV wind tunnel test is conducted.It was found that VIV is obviously on ?-section girder and mostly concentrated under the wind attack angle of +5°and +3°.Therefore,narrow the scope of study and choose the most unfavorable wind attack angle.The non-dimensional vertical amplitude,the reduced critical wind speed and the length of VIV lock-in were used as VIV performance indexes.The VIV performance of ?-section bare girder with different aspect ratio and the mitigation effect of wind fairing and lower stable plate were compared and analyzed.Besides,this paper explored the cause of double vertical VIV.As the most accurate method to study VIV,wind tunnel test in this paper provided a specific research direction and many reliable learning samples for the following text.(2)Using the user-defined function provided by Fluent software,the numerical stimulation of vertical VIV was programed based on Newmark-?,which was verified by the corresponding wind tunnel test results.This numerical method could provide abundant learning samples.According to learning samples,this paper performed a preliminary study about VIV performance of ?-section bare girder using two parameters,aspect ratio and opening ratio,and the mitigation effect of wind fairing and lower stable plate with different size.(3)According to the learning samples,the applicability of different ANN models were analyzed and the controlling parameters were optimized.Some ANN models suitable for VIV ?-section girder performance prediction,fitting and classification were established,which can quickly and accurately identity the VIV performance of ?-section girder of cable-stayed bridge.