Bridge model updating based on radial basis function neural network

A model updating method based on radial basis function neural network (RBF) is proposed for an ANSYS finite element model of a prestressed large span rigid-continuous concrete bridge. This method utilized the finite element modal analysis under different design parameter conditions as input vector, and the elastic modulus, density of box girders, piers as output vector. The nonlinear relationship between the inputs and outputs is approximated through RBF. With the generalization of neutral network, combined with bridge dynamic response monitored by acceleration sensors from the bridge structural health monitoring system, the corrected value of the finite element model of the design parameters is calculated. The research shows that the updated model can present the true physical conditions and the results of updated model better reflect dynamic characteristics of the bridge structure.