Closed Form Solution for Deflection of Flexible Composite Bridges

Abstract

Simply supported steel-concrete composite beams are widely used in bridge construction. Deflection is the significant parameter for serviceability limit state of bridges. A lot of computational effort is required for finite element analysis of bridges considering flexibility of shear connectors and shear leg effects. Neural network is presented for prediction of deflections, at service load, in simply supported steel-concrete composite bridges incorporating flexibility of shear connectors and shear lag effect. The training, testing and validation data sets for neural network are generated using finite element models. The finite element models have been developed using ABAQUS software. These models have been validated with available experimental results. Closed form solution is also proposed based on the developed neural network. The use of the neural network requires a computational effort almost equal to that required for the simple beam analysis (neglecting flexibility of shear connectors and shear lag effect). The neural network has been validated for number of bridges and the errors are found to be small. The network/closed form solution can be used for rapid prediction of deflection for everyday design.