基于高斯过程潜在力模型的结构参数-荷载联合识别方法OA

This study presents a joint identification method for structural parameters,input loads,and system states based on the Gaussian process latent force model(GPLFM).The structural parameter identification is integrated into the hyperparameter optimization process of GPLFM,thereby avoiding additional model updating steps,and the load identification accuracy significantly outperforms that of mainstream Bayesian filtering methods.In the proposed method,the unknown load time history is first modeled as a Gaussian process.By converting the Gaussian process regression into a linear state-space model,the numerical model,measurement data,and prior information of input loads are organically integrated.A Bayesian filter is then employed to estimate input loads and system states,while structural parameters are treated as hyperparameters and optimized within the Gaussian process framework via an energy function and Markov Chain Monte Carlo(MCMC)sampling,thus achieving structural parameter identification.The effectiveness of the method in identifying structural parameters,input loads,and system states is validated through a numerical simulation of a 10-story shear frame,a vibration test on a 3-story frame,and comparisons with the augmented Kalman filter(AKF)and the dual Kalman filter(DKF).