Structural Nonlinear Model Updating Based on an Improved Generative Adversarial Network

This study proposes a novel nonlinear model updating approach based on an improved generative adversarial network (GAN). In the improved GAN, a convolutional neural network (CNN) surrogate model is added to the discriminator network to enhance the capability of the GAN to learn the complex mapping relationship between vibration responses and nonlinear model parameters. To avoid the gradient disappearance present in the traditional GAN, a combined objective function is added to the improved GAN model. In the network training process, the instantaneous amplitudes of the decomposed accelerations are extracted as input samples and the nonlinear model parameters are defined as the GAN output. When the improved GAN is trained, the trained network model is capable of estimating the nonlinear model parameters based on measured instantaneous acceleration amplitudes. To confirm the feasibility of the improved GAN for structural nonlinear model updating, a steel-concrete hybrid bridge tower subjected to seismic excitation is numerically simulated and the effects of different numbers of data points and noise levels are studied. Furthermore, the identification accuracy of the improved GAN is compared with the updated results. For experimental applications, the shake table test of a scaled steel-concrete hybrid bridge tower subjected to seismic excitations is employed to confirm the effectiveness of the proposed nonlinear model updating method. Both numerical and experimental results demonstrate that the improved GAN model is reliable and effective for the nonlinear model updating of structures subjected to seismic excitation.