Deformation prediction model of large-span prestressed structure for health monitoring based on robust Gaussian process regression

Advanced structural health monitoring systems have been widely applied to large-span structures for obtaining various structural responses and loads, which are the foundation of performing condition assessment. The structural deformation is continuously employed to estimate the structural condition because it directly provides information about the overall stiffness of the whole structure. Therefore, accurate prediction of structural deformation is essential for reliable assessment of structural conditions. The deformation variation of a largespan prestressed structure is characterized by typical geometric nonlinear effects. This work presents a robust Gaussian process regression (GPR) for building a deformation prediction model for large-span space structures. The proposed approach overcomes the problem of computational cost and employs optimal distribution for modeling noise in monitoring data. Specifically, the PCA method is utilized to reduce the dimension of the input datasets for GPR. The optimal input dataset and noise distribution are estimated via 4 indexes, which are introduced to estimate the prediction performance of deformation prediction models. Simulated structural deformation from Hangzhou Gymnasium is used for verifying the effectiveness of the GPR-based deformation prediction models. Then, the proposed method is employed to predict the vertical deformation of the National Speed Skating Oval (NSSO) during snowfall. Furthermore, the prediction performance of the prediction model is comprehensively investigated via residual analysis. The proposed prediction model could provide a data foundation for the condition assessment of prestressed large-span structures.