Prediction of Axial Capacity of Concrete-Filled Square Steel Tubes Using Neural Networks

The application of artificial neural network to predict the ultimate bearing capacity of CFST （ concrete-filled square steel tubes） short columns under axial loading is explored. Input parameters consiste of concrete compressive strength, yield strength of steel tube, confinement index, sectional dimension and width-to-thickness ratio. The ultimate bearing capacity is the only output parameter. A multilayer feedforward neural network is used to describe the nonlinear relationships between the input and output variables. Fifty-five experimental data of CFST short columns under axial loading are used to train and test the neural network. A comparison between the neural network model and three parameter models shows that the neural network model possesses good accuracy and could be a practical method for predicting the ultimate strength of axially loaded CFST short columns.     

Fatigue Damage Modeling for Partially Prestressed Concrete Beams under Repeated Loadings with Variable Amplitude

Introduction Prestressedconcretestructuresfrequentlysub jectedtovariable amplituderepeatedloadingsare widelyusedinbridges,cranebeamsandoffshore structures.Repeatedloadingsmaycausetheprogres siveinternalstructuraldamagedegradingthereliabili tyofstructuresd…